recommender-systems/05_multimodality.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"xLXWx4uhqtYJ"},"source":["<table class=\"tfo-notebook-buttons\" align=\"left\">\n","  <td>\n","    <a target=\"_blank\" href=\"https://colab.research.google.com/github/PreferredAI/tutorials/blob/master/recommender-systems/05_multimodality.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n","  </td>\n","  <td>\n","    <a target=\"_blank\" href=\"https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/05_multimodality.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n","  </td>\n","</table>"]},{"cell_type":"markdown","metadata":{"id":"wzPrmZOMtXt1"},"source":["# Multimodality"]},{"cell_type":"markdown","metadata":{"id":"Bh3gYub8f6qZ"},"source":["While preference data in the form of user-item interactions are the backbone of many recommender systems, such data tends to be sparse in nature. One way to address this sparsity is to look beyond the interaction data to the additional information associated with users or with items. The intuition is that items with similarity in \"content profiles\" would be correlated with similarity in preferences. Multimodality deals with how to model both preference data (one modality) and some content data either on user or item side (other modalities). In this tutorial, we see three forms of additional modalities, namely graph, text, and image, and investigate whether they add value to the the resulting recommendations."]},{"cell_type":"markdown","metadata":{"id":"MqktDa7H2hKz"},"source":["## 1. Setup"]},{"cell_type":"code","execution_count":1,"metadata":{"id":"41oWCMUG2eC_","executionInfo":{"status":"ok","timestamp":1744701106568,"user_tz":-480,"elapsed":4229,"user":{"displayName":"Jaime Hieu Do","userId":"16382807970500480792"}}},"outputs":[],"source":["!pip install --quiet cornac==2.3.2"]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YqKrDcGH2k7E","outputId":"14e92d86-53be-4832-dcbf-ec9f0f8f25f0","executionInfo":{"status":"ok","timestamp":1744701113904,"user_tz":-480,"elapsed":7328,"user":{"displayName":"Jaime Hieu Do","userId":"16382807970500480792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["System version: 3.11.12 (main, Apr  9 2025, 08:55:54) [GCC 11.4.0]\n","Cornac version: 2.3.2\n","Tensorflow version: 2.18.0\n"]}],"source":["import os\n","import sys\n","from collections import defaultdict\n","\n","import numpy as np\n","import pandas as pd; pd.set_option(\"max_colwidth\", 0);\n","%matplotlib inline\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","\n","import cornac\n","from cornac.utils import cache\n","from cornac.datasets import filmtrust, amazon_clothing\n","from cornac.eval_methods import RatioSplit\n","from cornac.models import PMF, SoRec, WMF, CTR, BPR, VBPR\n","from cornac.data import GraphModality, TextModality, ImageModality\n","from cornac.data.text import BaseTokenizer\n","\n","import tensorflow as tf\n","\n","print(f\"System version: {sys.version}\")\n","print(f\"Cornac version: {cornac.__version__}\")\n","print(f\"Tensorflow version: {tf.__version__}\")\n","\n","SEED = 42\n","VERBOSE = False"]},{"cell_type":"markdown","metadata":{"id":"r76coF3ZyWxb"},"source":["## 2. Graph Modality\n","\n","In recommender systems, graph can be used to represent user social network or item contexts (e.g., co-views, co-purchases).  In this tutorial, we take the former as an example and discuss SoRec [2], a representative model for this class of algorithms.\n"]},{"cell_type":"markdown","metadata":{"id":"0GePqE99j5nr"},"source":["### Social Recommendation (SoRec)\n","\n","SoRec model is based on matrix factorization framework. The idea is fusing user-item rating matrix with the user’s social network.  In summary, the *user-item rating matrix* ($R$) and the *user-user graph adjacency matrix* ($G$) are factorized with shared users' latent factors.  The user latent vectors in $\\mathbf{U}$ are shared to capture both user preferences as well as their social connections.  The rating prediction is obtained as $\\hat{r}_{i,j} = \\mathbf{u}_i^T \\mathbf{v}_j$, similarly to PMF model.\n","\n","To learn the model parameters, we minimize the following loss function:\n","\n","$$ \\mathcal{L}(\\mathbf{U,V,Z}|\\lambda,\\lambda_C) = \\frac{1}{2} \\sum_{r_{i,j} \\in \\mathcal{R}} (r_{i,j} - \\mathbf{u}_i^T \\mathbf{v}_j)^2 + \\frac{\\lambda_C}{2} \\sum_{g_{i,h} \\in \\mathcal{G}} (g_{i,h} - \\mathbf{u}_i^T \\mathbf{z}_h)^2 + \\frac{\\lambda}{2} \\sum_{i=1}^{N} ||\\mathbf{u}_i||^2 + \\frac{\\lambda}{2} \\sum_{j=1}^{M} ||\\mathbf{v}_j||^2 + \\frac{\\lambda}{2} \\sum_{h=1}^{N} ||\\mathbf{z}_h||^2 $$\n","\n","where $\\lambda_C$ is the relative importance of the social network factorization and $\\lambda$ is the regularization weight.\n","\n","Let's do a comparison between SoRec and its base model PMF on [FilmTrust dataset](http://konect.cc/networks/librec-filmtrust-trust/)."]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"p2iyzuFFs1Fq","outputId":"8801cf09-527b-448f-adcd-4efbf4450ec3","executionInfo":{"status":"ok","timestamp":1744701118208,"user_tz":-480,"elapsed":4302,"user":{"displayName":"Jaime Hieu Do","userId":"16382807970500480792"}}},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.11/dist-packages/cornac/data/dataset.py:335: UserWarning: 3 duplicated observations are removed!\n","  warnings.warn(\"%d duplicated observations are removed!\" % dup_count)\n"]},{"output_type":"stream","name":"stdout","text":["\n","TEST:\n","...\n","      |    MAE | Train (s) | Test (s)\n","----- + ------ + --------- + --------\n","SoRec | 0.6644 |    1.2323 |   0.4017\n","PMF   | 0.7018 |    1.9206 |   0.3182\n","\n"]}],"source":["K = 20\n","sorec = SoRec(k=K, max_iter=50, learning_rate=0.001, verbose=VERBOSE, seed=SEED)\n","pmf = PMF(k=K, max_iter=50, learning_rate=0.001, lambda_reg=0.01, verbose=VERBOSE, seed=SEED)\n","\n","ratings = filmtrust.load_feedback()\n","trust = filmtrust.load_trust()\n","\n","user_graph_modality = GraphModality(data=trust)\n","\n","ratio_split = RatioSplit(\n","    data=ratings,\n","    test_size=0.2,\n","    rating_threshold=2.5,\n","    exclude_unknowns=True,\n","    user_graph=user_graph_modality,\n","    verbose=VERBOSE,\n","    seed=SEED,\n",")\n","\n","mae = cornac.metrics.MAE()\n","\n","cornac.Experiment(eval_method=ratio_split, models=[sorec, pmf], metrics=[mae]).run()"]},{"cell_type":"markdown","metadata":{"id":"2oQ7J1_r2CGT"},"source":["From the experiment, we see that SoRec achieves lower (better) MAE score as compared to PMF.  This improvement should be explained by useful information from user social network captured inside the model predictions.  Let's take a look at user factors and see if there are connections to the social network.  "]},{"cell_type":"markdown","metadata":{"id":"QUFnKYm-uWLh"},"source":["### Interpreting Recommendation\n","\n","Below is the variance of each user latent dimension."]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":482},"id":"01086B3oza3N","outputId":"7e44e937-a963-451c-96e7-b300ab7eeb42","executionInfo":{"status":"ok","timestamp":1744701118834,"user_tz":-480,"elapsed":624,"user":{"displayName":"Jaime Hieu Do","userId":"16382807970500480792"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<Axes: xlabel='Factor', ylabel='Variance'>"]},"metadata":{},"execution_count":4},{"output_type":"display_data","data":{"text/plain":["<Figure size 1300x500 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["var_df = pd.DataFrame({\"Factor\": np.arange(K), \"Variance\": np.var(sorec.U, axis=0)})\n","fig, ax = plt.subplots(figsize=(13, 5))\n","sns.barplot(x=\"Factor\", y=\"Variance\", data=var_df, palette=\"ch:.25\", ax=ax, hue=\"Factor\", legend=False)"]},{"cell_type":"markdown","metadata":{"id":"aizVEPmHuh8n"},"source":["Let's select two dimensions and see how users are distributed in that 2D space.  In addition, we can visualize connections between two users if they are linked in the social network."]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"ndiIw1WJ8Ghv","outputId":"a4a8f750-41d3-4de5-c3bb-a25e37ee8e21","executionInfo":{"status":"ok","timestamp":1744701122826,"user_tz":-480,"elapsed":3990,"user":{"displayName":"Jaime Hieu Do","userId":"16382807970500480792"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1100x1100 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["TOP2F = (9, 19)\n","SAMPLE_SIZE = 200\n","\n","rng = np.random.RandomState(SEED)\n","sample_inds = rng.choice(np.arange(sorec.U.shape[0]), size=SAMPLE_SIZE, replace=False)\n","sample_df = pd.DataFrame(data=sorec.U[sample_inds][:, TOP2F], columns=[\"x\", \"y\"])\n","g = sns.lmplot(x=\"x\", y=\"y\", data=sample_df, height=11.0, fit_reg=False)\n","g.ax.set_title(\"Users in latent space with their social connections\", fontsize=16)\n","\n","adj_mat = sorec.train_set.user_graph.matrix\n","for i in range(len(sample_inds)):\n","  for j in range(len(sample_inds)):\n","    if j != i and adj_mat[sample_inds[i], sample_inds[j]]:\n","      sns.lineplot(x=\"x\", y=\"y\", data=sample_df.loc[[i, j]])"]},{"cell_type":"markdown","metadata":{"id":"lNWQ6mDFu3uv"},"source":["There are indeed some locality of user connections shown in the figure above. That suggests social network information is captured in the user latent factors and has some influence on the recommendations by the SoRec model."]},{"cell_type":"markdown","metadata":{"id":"-8iXcfuKsVa8"},"source":["## 3. Text Modality\n","\n","Often times, we are interested in building a recommender system for textual items (e.g., news, scientific papers), or items associated with text (e.g., titles, descriptions, reviews).  Text is informative and descriptive, therefore, exploiting textual information for better recommendations is an important topic in recommender systems.  In this tutorial, we introduce CTR [3], a recommendation model that combines matrix factorization and probablistic topic modeling.\n","\n"]},{"cell_type":"markdown","metadata":{"id":"742yJ53Fj1Zz"},"source":["### Collaborative Topic Regression (CTR)\n","\n","Under factorization framework, adoption prediction is in the form of $\\hat{r}_{i,j} = \\mathbf{u}_i^T \\mathbf{v}_j $.  The intuition in CTR model is that two items with similar topics would behave similarly. Thus, item latent factors $\\mathbf{v_j}$ is assumed to be drawn from a Normal distribution:\n","\n","$$\n","\\mathbf{v}_j \\sim \\mathcal{N}(\\mathbf{\\theta}_j, \\lambda^{-1} \\mathbf{I})\n","$$\n","\n","where the mean $\\mathbf{\\theta}_j$ is a vector indicating topic proportions of the item $j$. It is equivalent to:\n","\n","\\begin{align}\n","\\mathbf{v}_j &= \\mathbf{\\theta}_j + \\mathbf{\\epsilon}_j \\\\\n","\\mathbf{\\epsilon}_j &\\sim \\mathcal{N}(\\mathbf{0}, \\lambda^{-1} \\mathbf{I})\n","\\end{align}\n","\n","Please refer to paper [3] for the generative process of CTR model.\n","\n","\n","CTR also extends matrix factorization, in which the base model is WMF under implicit feedback setting. The adoption $p_{i,j}$ and confidence $c_{i,j}$ are defined as follows:\n","\n","\\begin{equation}\n","p_{i,j} =\n","\\begin{cases}\n","r_{i, j} &\\mbox{if } r_{i,j} > 0 \\\\\n","0 & \\mbox{otherwise}\n","\\end{cases}\n","\\end{equation}\n","\n","\n","\\begin{equation}\n","c_{i,j} =\n","\\begin{cases}\n","a & \\mbox{if } r_{i,j} > 0 \\\\\n","b & \\mbox{otherwise }\n","\\end{cases}\n","\\end{equation}\n","\n","The learning of CTR model is done via minimizing the following negative log-likelihood:\n","\n","$$ \\mathcal{L}(\\mathbf{U,V,\\theta, \\beta}|\\lambda) = \\frac{1}{2} \\sum_{i,j} c_{i,j} (p_{i,j} - \\mathbf{u}_i^T \\mathbf{v}_j)^2 - \\sum_{j}\\sum_{n} \\log \\big( \\sum_{k=1}^K \\mathbf{\\theta}_{j,k} \\mathbf{\\beta}_{k,w_{jn}} \\big) + \\frac{\\lambda}{2} \\sum_{i=1}^{N} ||\\mathbf{u}_i||^2 + \\frac{\\lambda}{2} \\sum_{j=1}^{M} (\\mathbf{v}_j - \\mathbf{\\theta}_j)^T (\\mathbf{v}_j - \\mathbf{\\theta}_j) $$\n","\n","It is an iterative procedure of alternating between three steps:\n","- Optimize for user and item latent vectors, $\\mathbf{u}_i$ and $\\mathbf{v}_j$, based on the current topic proportions $\\mathbf{\\theta}_j$.  \n","- Optimize for topic proportions $\\mathbf{\\theta}_j$ based on the current vectors $\\mathbf{u}_i$ and $\\mathbf{v}_j$ and topic words $\\mathbf{\\beta}_k$.\n","- Optimize for topic words $\\mathbf{\\beta}_k$ based on the current topic proportions $\\mathbf{\\theta}_i$.\n","\n","Let's experiment with two models CTR and WMF on a dataset from Amazon Clothing category.  Using this dataset, CTR will learn topics from item description.  "]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NZ2Al6omrlEP","outputId":"1bd60e8a-a377-4fcf-cbce-26fb420a10a4","executionInfo":{"status":"ok","timestamp":1744701194994,"user_tz":-480,"elapsed":72166,"user":{"displayName":"Jaime Hieu Do","userId":"16382807970500480792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","TEST:\n","...\n","    | Recall@50 | Train (s) | Test (s)\n","--- + --------- + --------- + --------\n","CTR |    0.2263 |   56.9298 |   0.4644\n","WMF |    0.1606 |   13.2753 |   0.4695\n","\n"]}],"source":["K = 20\n","ctr = CTR(k=K, max_iter=50, a=1.0, b=0.01, lambda_u=0.01, lambda_v=0.01, verbose=VERBOSE, seed=SEED)\n","wmf = WMF(k=K, max_iter=50, a=1.0, b=0.01, learning_rate=0.005, lambda_u=0.01, lambda_v=0.01,\n","          verbose=VERBOSE, seed=SEED)\n","\n","ratings = amazon_clothing.load_feedback()\n","docs, item_ids = amazon_clothing.load_text()\n","\n","item_text_modality = TextModality(\n","    corpus=docs,\n","    ids=item_ids,\n","    tokenizer=BaseTokenizer(sep=\" \", stop_words=\"english\"),\n","    max_vocab=8000,\n","    max_doc_freq=0.5,\n",")\n","\n","ratio_split = RatioSplit(\n","    data=ratings,\n","    test_size=0.2,\n","    rating_threshold=4.0,\n","    exclude_unknowns=True,\n","    item_text=item_text_modality,\n","    verbose=VERBOSE,\n","    seed=SEED,\n",")\n","\n","rec_50 = cornac.metrics.Recall(50)\n","\n","cornac.Experiment(eval_method=ratio_split, models=[ctr, wmf], metrics=[rec_50]).run()"]},{"cell_type":"markdown","metadata":{"id":"uofgWV3oFLRq"},"source":["The results show that CTR model performs significantly better than WMF model in terms of Recall@50, which is due to the contribution of items' textual information."]},{"cell_type":"markdown","metadata":{"id":"r-8mCmABFe8e"},"source":["### Interpreting Recommendation\n","\n","As CTR is based on topic model, it has intuitive explainability both in terms of model parameters as well as its recommendations. First, we can see what the top words of each topic are, based on learned topic-word distributions $\\mathbf{\\beta}$:"]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":694},"id":"fOY0pdwcC-VD","outputId":"cf972cff-afa0-4647-a4f2-bb1d67a48bb6","executionInfo":{"status":"ok","timestamp":1744701195010,"user_tz":-480,"elapsed":22,"user":{"displayName":"Jaime Hieu Do","userId":"16382807970500480792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["WORD TOPICS:\n"]},{"output_type":"execute_result","data":{"text/plain":["    Topic  \\\n","0   1       \n","1   2       \n","2   3       \n","3   4       \n","4   5       \n","5   6       \n","6   7       \n","7   8       \n","8   9       \n","9   10      \n","10  11      \n","11  12      \n","12  13      \n","13  14      \n","14  15      \n","15  16      \n","16  17      \n","17  18      \n","18  19      \n","19  20      \n","\n","                                                                                  Top words  \n","0   belt, flatlock, case, size, better, choose, earrings, soft, warm, collar                 \n","1   shirt, style, breathable, princess, youre, colors, quality, come, people, socks          \n","2   easy, collection, colors, rubber, water, quickly, stretch, lightweight, design, body     \n","3   activities, handle, helmet, star, way, flat, makes, day, half, tee                       \n","4   fit, sweat, mesh, large, pocket, fleece, body, necklace, imported, travel                \n","5   wicks, material, dry, fit, panels, piece, insect, construction, durability, standard     \n","6   support, company, fabric, apparel, fun, sizes, hip, outdoor, fiber, features             \n","7   x, merino, sleeve, fun, items, water, weather, bracelets, comfortable, display           \n","8   great, performance, black, pockets, inches, make, away, design, jewelry, unique          \n","9   fits, sun, store, engineered, pocket, shield, conditions, mobility, hat, maximum         \n","10  costume, products, small, inside, classic, lightweight, comfort, white, ideal, designed  \n","11  bag, elastic, jacket, perfect, favorite, hat, d, box, look, feature                      \n","12  s, use, sure, soft, comfort, easy, products, skin, displays, moisture                    \n","13  size, comfortable, product, warm, need, ua, accessory, clean, footwear, silver           \n","14  high, storage, height, waistband, wide, way, chest, include, growth, new                 \n","15  delivers, tray, nylon, sturdy, free, pirate, builtin, allows, fairy, wear                \n","16  cool, shoulder, provide, x, cold, prevents, womens, color, beautiful, years              \n","17  pockets, mask, durable, moisture, jewelry, features, new, offers, comfort, brushed       \n","18  straps, clothing, metal, way, microbes, shoes, silver, easily, toe, polyester            \n","19  complete, wear, fabric, features, display, measures, merino, technology, gift, want      "],"text/html":["\n","  <div id=\"df-4393908e-d129-4a3a-b493-ca1e9f236758\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Topic</th>\n","      <th>Top words</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>belt, flatlock, case, size, better, choose, earrings, soft, warm, collar</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>shirt, style, breathable, princess, youre, colors, quality, come, people, socks</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>easy, collection, colors, rubber, water, quickly, stretch, lightweight, design, body</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>activities, handle, helmet, star, way, flat, makes, day, half, tee</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>fit, sweat, mesh, large, pocket, fleece, body, necklace, imported, travel</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>6</td>\n","      <td>wicks, material, dry, fit, panels, piece, insect, construction, durability, standard</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>7</td>\n","      <td>support, company, fabric, apparel, fun, sizes, hip, outdoor, fiber, features</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>8</td>\n","      <td>x, merino, sleeve, fun, items, water, weather, bracelets, comfortable, display</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>9</td>\n","      <td>great, performance, black, pockets, inches, make, away, design, jewelry, unique</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>10</td>\n","      <td>fits, sun, store, engineered, pocket, shield, conditions, mobility, hat, maximum</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>11</td>\n","      <td>costume, products, small, inside, classic, lightweight, comfort, white, ideal, designed</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>12</td>\n","      <td>bag, elastic, jacket, perfect, favorite, hat, d, box, look, feature</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>13</td>\n","      <td>s, use, sure, soft, comfort, easy, products, skin, displays, moisture</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>14</td>\n","      <td>size, comfortable, product, warm, need, ua, accessory, clean, footwear, silver</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>15</td>\n","      <td>high, storage, height, waistband, wide, way, chest, include, growth, new</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>16</td>\n","      <td>delivers, tray, nylon, sturdy, free, pirate, builtin, allows, fairy, wear</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>17</td>\n","      <td>cool, shoulder, provide, x, cold, prevents, womens, color, beautiful, years</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>18</td>\n","      <td>pockets, mask, durable, moisture, jewelry, features, new, offers, comfort, brushed</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>19</td>\n","      <td>straps, clothing, metal, way, microbes, shoes, silver, easily, toe, polyester</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>20</td>\n","      <td>complete, wear, fabric, features, display, measures, merino, technology, gift, want</td>\n","    </tr>\n","  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ingore 4 special tokens\n","\n","topic_words = {}\n","topic_df = defaultdict(list)\n","print(\"WORD TOPICS:\")\n","for t in range(len(topic_word_dist)):\n","  top_words = vocab.to_text(top_word_inds[t][-10:][::-1], sep=\", \")\n","  topic_words[t+1] = top_words\n","  topic_df[\"Topic\"].append(t + 1)\n","  topic_df[\"Top words\"].append(top_words)\n","topic_df = pd.DataFrame(topic_df)\n","topic_df"]},{"cell_type":"markdown","metadata":{"id":"eHmSmoWnG8jX"},"source":["As item vectors are close to its topic proportions, user vectors can also be used to explain what topics they are interested in."]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":161},"id":"lTs0zjSmcEOK","outputId":"9ef505c6-27ea-4141-a8ed-195d43707c9f","executionInfo":{"status":"ok","timestamp":1744701195020,"user_tz":-480,"elapsed":10,"user":{"displayName":"Jaime Hieu Do","userId":"16382807970500480792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["USER 123 TOP-3 TOPICS:\n"]},{"output_type":"execute_result","data":{"text/plain":["    Topic  \\\n","14  15      \n","13  14      \n","12  13      \n","\n","                                                                         Top words  \n","14  high, storage, height, waistband, wide, way, chest, include, growth, new        \n","13  size, comfortable, product, warm, need, ua, accessory, clean, footwear, silver  \n","12  s, use, sure, soft, comfort, easy, products, skin, displays, moisture           "],"text/html":["\n","  <div id=\"df-3b7b7f3d-4460-419b-8681-321582abb533\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Topic</th>\n","      <th>Top words</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>14</th>\n","      <td>15</td>\n","      <td>high, storage, height, waistband, wide, way, chest, include, growth, new</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>14</td>\n","      <td>size, comfortable, product, warm, need, ua, accessory, clean, footwear, silver</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>13</td>\n","      <td>s, use, sure, soft, comfort, easy, products, skin, displays, moisture</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3b7b7f3d-4460-419b-8681-321582abb533')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-3b7b7f3d-4460-419b-8681-321582abb533 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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What will be recommendations to this user?"]},{"cell_type":"code","execution_count":9,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":310},"id":"7jHH_y_iH6Bt","outputId":"f87d10e9-9501-4845-c65d-dd7373d91130","executionInfo":{"status":"ok","timestamp":1744701195030,"user_tz":-480,"elapsed":9,"user":{"displayName":"Jaime Hieu Do","userId":"16382807970500480792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","TOP 5 RECOMMENDATIONS FOR USER 123:\n"]},{"output_type":"execute_result","data":{"text/plain":["                                    URL  \\\n","0  https://www.amazon.com/dp/B002M62ES6   \n","1  https://www.amazon.com/dp/B002MKPKFG   \n","2  https://www.amazon.com/dp/B001NAHW2U   \n","3  https://www.amazon.com/dp/B000HS6NEC   \n","4  https://www.amazon.com/dp/B001EDF3OU   \n","\n","                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               Description  \n","0  Accessorize your Spider-man Costume with these Gloves. Fits most child sizes from 4-12.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  \n","1  This glamorous dress is perfect for a night at the ball. The stretchy blue velvet bodice is elegantly detailed with a gathered white inlay, ruffled cap sleeves, sparkling silver trim and a white rose. The silver trimmed waist drapes outlined with ruffle capture that authentic Cinderella look.The dress is designed with an empire waist that sits 1.5 inches above the waist. They are machine washable and made for long repeated use, unlike many one-off Halloween costumes that are not made for everyday play.  Our stretchy accommodating fabrics will allow for a comfortable fit for each child within the respective age ranges and there are no itchy fabrics or trims.Each dress comes with an exclusive Princess Hairbow as part of the bundle set.  \n","2  LACY 14K GOLD on 925 Silver Charm BEAD compatible with European Bracelet                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 \n","3  Includes jacket, elastic pants, hat, belt, whistle, and gun holster. Child Size Small (4-6).                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             \n","4  The Child Indiana Jones Hat and Whip are two must have accessories for your child's Officially Licensed Indiana Jones costume. This Indiana Jones costume accessory contains the two hallmarks of Indy's style: his awesome whip and his trademark hat.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  "],"text/html":["\n","  <div id=\"df-cd7bf8c9-d696-48a2-a31e-600ba430997c\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>URL</th>\n","      <th>Description</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>https://www.amazon.com/dp/B002M62ES6</td>\n","      <td>Accessorize your Spider-man Costume with these Gloves. Fits most child sizes from 4-12.</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>https://www.amazon.com/dp/B002MKPKFG</td>\n","      <td>This glamorous dress is perfect for a night at the ball. The stretchy blue velvet bodice is elegantly detailed with a gathered white inlay, ruffled cap sleeves, sparkling silver trim and a white rose. The silver trimmed waist drapes outlined with ruffle capture that authentic Cinderella look.The dress is designed with an empire waist that sits 1.5 inches above the waist. They are machine washable and made for long repeated use, unlike many one-off Halloween costumes that are not made for everyday play.  Our stretchy accommodating fabrics will allow for a comfortable fit for each child within the respective age ranges and there are no itchy fabrics or trims.Each dress comes with an exclusive Princess Hairbow as part of the bundle set.</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>https://www.amazon.com/dp/B001NAHW2U</td>\n","      <td>LACY 14K GOLD on 925 Silver Charm BEAD compatible with European Bracelet</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>https://www.amazon.com/dp/B000HS6NEC</td>\n","      <td>Includes jacket, elastic pants, hat, belt, whistle, and gun holster. Child Size Small (4-6).</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>https://www.amazon.com/dp/B001EDF3OU</td>\n","      <td>The Child Indiana Jones Hat and Whip are two must have accessories for your child's Officially Licensed Indiana Jones costume. This Indiana Jones costume accessory contains the two hallmarks of Indy's style: his awesome whip and his trademark hat.</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cd7bf8c9-d696-48a2-a31e-600ba430997c')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-cd7bf8c9-d696-48a2-a31e-600ba430997c button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-13624512-d26a-4acb-a4d8-00d76844e244\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-13624512-d26a-4acb-a4d8-00d76844e244')\"\n","            title=\"Suggest charts\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\n","  <script>\n","    async function quickchart(key) {\n","      const quickchartButtonEl =\n","        document.querySelector('#' + key + ' button');\n","      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n","      quickchartButtonEl.classList.add('colab-df-spinner');\n","      try {\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      } catch (error) {\n","        console.error('Error during call to suggestCharts:', error);\n","      }\n","      quickchartButtonEl.classList.remove('colab-df-spinner');\n","      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n","    }\n","    (() => {\n","      let quickchartButtonEl =\n","        document.querySelector('#df-13624512-d26a-4acb-a4d8-00d76844e244 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","\n","    </div>\n","  </div>\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","summary":"{\n  \"name\": \"pd\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"URL\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"https://www.amazon.com/dp/B002MKPKFG\",\n          \"https://www.amazon.com/dp/B001EDF3OU\",\n          \"https://www.amazon.com/dp/B001NAHW2U\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Description\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"This glamorous dress is perfect for a night at the ball. The stretchy blue velvet bodice is elegantly detailed with a gathered white inlay, ruffled cap sleeves, sparkling silver trim and a white rose. The silver trimmed waist drapes outlined with ruffle capture that authentic Cinderella look.The dress is designed with an empire waist that sits 1.5 inches above the waist. They are machine washable and made for long repeated use, unlike many one-off Halloween costumes that are not made for everyday play.  Our stretchy accommodating fabrics will allow for a comfortable fit for each child within the respective age ranges and there are no itchy fabrics or trims.Each dress comes with an exclusive Princess Hairbow as part of the bundle set.\",\n          \"The Child Indiana Jones Hat and Whip are two must have accessories for your child's Officially Licensed Indiana Jones costume. This Indiana Jones costume accessory contains the two hallmarks of Indy's style: his awesome whip and his trademark hat.\",\n          \"LACY 14K GOLD on 925 Silver Charm BEAD compatible with European Bracelet\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":9}],"source":["recommendations, scores = ctr.rank(UIDX)\n","print(f\"\\nTOP {TOPK} RECOMMENDATIONS FOR USER {UIDX}:\")\n","rec_df = defaultdict(list)\n","for i in recommendations[:TOPK]:\n","  rec_df[\"URL\"].append(f\"https://www.amazon.com/dp/{item_idx2id[i]}\")\n","  rec_df[\"Description\"].append(ctr.train_set.item_text.corpus[i])\n","pd.DataFrame(rec_df)"]},{"cell_type":"markdown","metadata":{"id":"L2E8LxyoIuZa"},"source":["Interestingly, in the desctiption of top-5 recommendations, we see words relevant to the user's topics (e.g., *Accessorize, Gloves, Bracelet, Silver*). This would suggest that CTR model can capture user preferences via the notions of item topics."]},{"cell_type":"markdown","metadata":{"id":"0jb-o9QJKcaS"},"source":["---\n","\n","Let's check out another user with *index = 321*. Similarly, we see the top-3 topics this user cares most about:"]},{"cell_type":"code","execution_count":10,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":161},"id":"aKowd8UQQjkT","outputId":"0ab2ee3c-bee4-4d93-bc33-856f0bd9fe97","executionInfo":{"status":"ok","timestamp":1744701195039,"user_tz":-480,"elapsed":8,"user":{"displayName":"Jaime Hieu Do","userId":"16382807970500480792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["USER 321 TOP-3 TOPICS:\n"]},{"output_type":"execute_result","data":{"text/plain":["    Topic  \\\n","19  20      \n","2   3       \n","10  11      \n","\n","                                                                                  Top words  \n","19  complete, wear, fabric, features, display, measures, merino, technology, gift, want      \n","2   easy, collection, colors, rubber, water, quickly, stretch, lightweight, design, body     \n","10  costume, products, small, inside, classic, lightweight, comfort, white, ideal, designed  "],"text/html":["\n","  <div id=\"df-4f53e958-6d3f-47fe-a797-c1464b936aad\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Topic</th>\n","      <th>Top words</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>19</th>\n","      <td>20</td>\n","      <td>complete, wear, fabric, features, display, measures, merino, technology, gift, want</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>easy, collection, colors, rubber, water, quickly, stretch, lightweight, design, body</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>11</td>\n","      <td>costume, products, small, inside, classic, lightweight, comfort, white, ideal, designed</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4f53e958-6d3f-47fe-a797-c1464b936aad')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-4f53e958-6d3f-47fe-a797-c1464b936aad button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","\n","    </div>\n","  </div>\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","summary":"{\n  \"name\": \"topic_df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Topic\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 3,\n        \"max\": 20,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          20,\n          3,\n          11\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Top words\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"complete, wear, fabric, features, display, measures, merino, technology, gift, want\",\n          \"easy, collection, colors, rubber, water, quickly, stretch, lightweight, design, body\",\n          \"costume, products, small, inside, classic, lightweight, comfort, white, ideal, designed\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":10}],"source":["UIDX = 321\n","\n","print(f\"USER {UIDX} TOP-3 TOPICS:\")\n","topic_df.loc[np.argsort(ctr.U[UIDX])[-3:][::-1]]"]},{"cell_type":"markdown","metadata":{"id":"29isliv_K0uf"},"source":["The topics include clothing with certain characteristics (*easy, water, stretch, lightweight, comfort*).\n","\n","Based on this, how would the model give recommendations to the user?"]},{"cell_type":"code","execution_count":11,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":397},"id":"z7mvxNntLo8V","outputId":"61138567-8e52-4add-d85e-87b398547989","executionInfo":{"status":"ok","timestamp":1744701195052,"user_tz":-480,"elapsed":12,"user":{"displayName":"Jaime Hieu Do","userId":"16382807970500480792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","TOP 5 RECOMMENDATIONS FOR USER 321:\n"]},{"output_type":"execute_result","data":{"text/plain":["                                    URL  \\\n","0  https://www.amazon.com/dp/B000MARQFA   \n","1  https://www.amazon.com/dp/B001VIH648   \n","2  https://www.amazon.com/dp/B002V44LTO   \n","3  https://www.amazon.com/dp/B002FIVZD6   \n","4  https://www.amazon.com/dp/B00062B1IC   \n","\n","                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   Description  \n","0  You demand a lot from your pants when you're on the move and the PROPPER Tactical Pant delivers. Nine pockets hold everything from your smartphone to your multi-tool. Rugged canvas fabric offers durability and strength, with DuPont Teflon fabric protector to repel stains and liquids. An action-stretch waistband adds comfort. And with our innovative wallet \"pocket in a pocket\" you'll appreciate not sitting on your wallet all day long. Come with a free matching belt (12.99 value).                                                                                                                          \n","1  You demand a lot from your pants when you're on the move and the PROPPER Tactical Pant delivers. Nine pockets hold everything from your smartphone to your multi-tool. Rugged canvas fabric offers durability and strength, with DuPont Teflon fabric protector to repel stains and liquids. An action-stretch waistband adds comfort. And with our innovative wallet \"pocket in a pocket\" you'll appreciate not sitting on your wallet all day long. Come with a free matching belt (12.99 value).                                                                                                                          \n","2  Sleeves - Made From 50% nylon & 50% cotton. Chest & Back - Made from 40% Acrylic & 60% Cotton. Resists melting & dripping. Melting point is rated to 230 degrees Fahrenheit. Moisture wicking. Lightweight & breathable. Zippered sleeve storage pockets. Mock turtle neck. Anti abrasion padded elbow patches. Gusseted sleeves. Glint tape holder for IR tape. Loop on sleeve pockets for attaching name, rank and flag. Colors Available: Black, ACU Digital Camouflage, Olive Drab, Desert Digital Camouflage & Digitial Woodland Camouflage. Sizes to choose from: Small, Medium, Large, X-Large, 2X-Large & 3X-Large.  \n","3  TRU-SPEC is one of the leading suppliers of uniforms and personal equipment to military, law enforcement and public safety markets.&#xA0; Thousands of police, firefighters, EMTs and military personnel use and depend on our products daily. The name TRU-SPEC has become synonymous with quality, innovation and service. Gunnery Sgt. R. Lee Ermey, USMC (Ret.) known as \"The Gunny\" has become the official celebrity spokesperson for TRU-SPEC's line of tactically inspired apparel.                                                                                                                                  \n","4  The PROPPER BDU trouser offers an authentic military cut in comfortable, durable Battle Rip fabric. Sewn to military specification, this button-fly BDU is fade, shrink and wrinkle resistant. It features felled seams and reinforced seat and knee for maximum durability. Six pockets with fused pocket flaps ensure a clean, professional look. With adjustable waist tabs and durable drawstring leg closures for a secure fit.                                                                                                                                                                                         "],"text/html":["\n","  <div id=\"df-6f06f573-0c12-4088-a584-39bfc5f0e3e3\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>URL</th>\n","      <th>Description</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>https://www.amazon.com/dp/B000MARQFA</td>\n","      <td>You demand a lot from your pants when you're on the move and the PROPPER Tactical Pant delivers. Nine pockets hold everything from your smartphone to your multi-tool. Rugged canvas fabric offers durability and strength, with DuPont Teflon fabric protector to repel stains and liquids. An action-stretch waistband adds comfort. And with our innovative wallet \"pocket in a pocket\" you'll appreciate not sitting on your wallet all day long. Come with a free matching belt (12.99 value).</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>https://www.amazon.com/dp/B001VIH648</td>\n","      <td>You demand a lot from your pants when you're on the move and the PROPPER Tactical Pant delivers. Nine pockets hold everything from your smartphone to your multi-tool. Rugged canvas fabric offers durability and strength, with DuPont Teflon fabric protector to repel stains and liquids. An action-stretch waistband adds comfort. And with our innovative wallet \"pocket in a pocket\" you'll appreciate not sitting on your wallet all day long. Come with a free matching belt (12.99 value).</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>https://www.amazon.com/dp/B002V44LTO</td>\n","      <td>Sleeves - Made From 50% nylon &amp; 50% cotton. Chest &amp; Back - Made from 40% Acrylic &amp; 60% Cotton. Resists melting &amp; dripping. Melting point is rated to 230 degrees Fahrenheit. Moisture wicking. Lightweight &amp; breathable. Zippered sleeve storage pockets. Mock turtle neck. Anti abrasion padded elbow patches. Gusseted sleeves. Glint tape holder for IR tape. Loop on sleeve pockets for attaching name, rank and flag. Colors Available: Black, ACU Digital Camouflage, Olive Drab, Desert Digital Camouflage &amp; Digitial Woodland Camouflage. Sizes to choose from: Small, Medium, Large, X-Large, 2X-Large &amp; 3X-Large.</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>https://www.amazon.com/dp/B002FIVZD6</td>\n","      <td>TRU-SPEC is one of the leading suppliers of uniforms and personal equipment to military, law enforcement and public safety markets.&amp;#xA0; Thousands of police, firefighters, EMTs and military personnel use and depend on our products daily. The name TRU-SPEC has become synonymous with quality, innovation and service. Gunnery Sgt. R. Lee Ermey, USMC (Ret.) known as \"The Gunny\" has become the official celebrity spokesperson for TRU-SPEC's line of tactically inspired apparel.</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>https://www.amazon.com/dp/B00062B1IC</td>\n","      <td>The PROPPER BDU trouser offers an authentic military cut in comfortable, durable Battle Rip fabric. Sewn to military specification, this button-fly BDU is fade, shrink and wrinkle resistant. It features felled seams and reinforced seat and knee for maximum durability. Six pockets with fused pocket flaps ensure a clean, professional look. With adjustable waist tabs and durable drawstring leg closures for a secure fit.</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6f06f573-0c12-4088-a584-39bfc5f0e3e3')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-6f06f573-0c12-4088-a584-39bfc5f0e3e3 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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Moisture wicking. Lightweight & breathable. Zippered sleeve storage pockets. Mock turtle neck. Anti abrasion padded elbow patches. Gusseted sleeves. Glint tape holder for IR tape. Loop on sleeve pockets for attaching name, rank and flag. Colors Available: Black, ACU Digital Camouflage, Olive Drab, Desert Digital Camouflage & Digitial Woodland Camouflage. Sizes to choose from: Small, Medium, Large, X-Large, 2X-Large & 3X-Large.\",\n          \"The PROPPER BDU trouser offers an authentic military cut in comfortable, durable Battle Rip fabric. Sewn to military specification, this button-fly BDU is fade, shrink and wrinkle resistant. It features felled seams and reinforced seat and knee for maximum durability. Six pockets with fused pocket flaps ensure a clean, professional look. With adjustable waist tabs and durable drawstring leg closures for a secure fit.\",\n          \"You demand a lot from your pants when you're on the move and the PROPPER Tactical Pant delivers. Nine pockets hold everything from your smartphone to your multi-tool. Rugged canvas fabric offers durability and strength, with DuPont Teflon fabric protector to repel stains and liquids. An action-stretch waistband adds comfort. And with our innovative wallet \\\"pocket in a pocket\\\" you'll appreciate not sitting on your wallet all day long. Come with a free matching belt (12.99 value).\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":11}],"source":["recommendations, scores = ctr.rank(UIDX)\n","print(f\"\\nTOP {TOPK} RECOMMENDATIONS FOR USER {UIDX}:\")\n","rec_df = defaultdict(list)\n","for i in recommendations[:TOPK]:\n","  rec_df[\"URL\"].append(f\"https://www.amazon.com/dp/{item_idx2id[i]}\")\n","  rec_df[\"Description\"].append(ctr.train_set.item_text.corpus[i])\n","pd.DataFrame(rec_df)"]},{"cell_type":"markdown","metadata":{"id":"1xQu4pblNZRq"},"source":["From the top-5 recommendations, we notice that those items are *military/outdoor/sports* clothing, based on their descriptions as well as the URL to Amazon website. That is also evident from the user's topics that we observe above."]},{"cell_type":"markdown","metadata":{"id":"q1AQfgGe8fyo"},"source":["## 4. Image Modality\n","\n","In some contexts, item images are informative (e.g., fashion). With the existence of effective methods to learn image representation, using item images in recommender systems is gaining popularity. In this tutorial, we present VBPR [4], a recommendation model making use of item image features extracted from pre-trained Convolutional Neural Network (CNN)."]},{"cell_type":"markdown","metadata":{"id":"A7WGq2xxjqjA"},"source":["### Visual Bayesian Personalized Ranking (VBPR)\n","\n","VBPR, which is also based on matrix factorization, is an extension of BPR model.  The novelty of VBPR is on how item visual features incorporated into the matrix factorization framework.  The preference score user $i$ giving to item $j$ is predicted as follows:\n","\n","$$\n","\\hat{r}_{i,j} = \\alpha + b_i + b_j + \\mathbf{u}_i^T \\mathbf{v}_j + \\mathbf{p}_{i}^T(\\mathbf{E} \\times \\mathbf{f}_j) + \\mathbf{\\Theta}^T \\mathbf{f}_j\n","$$\n","\n","where:\n","- $\\alpha, b_i, b_j$ are global bias, user bias, and item bias, respectively\n","- $\\mathbf{u}_i \\in \\mathbb{R}^K$ and $\\mathbf{v}_j \\in \\mathbb{R}^K$ are user and item latent vectors, respectively\n","- $\\mathbf{f}_j \\in \\mathbb{R}^D$ is the item image feature vector\n","- $\\mathbf{p}_i \\in \\mathbb{R}^Q$ is user visual preference, and $(\\mathbf{E} \\times \\mathbf{f}_j) \\in \\mathbb{R}^Q$ is item visual representation with $\\mathbf{E} \\in \\mathbb{R}^{K \\times D}$ is the projection from visual feature space into preference space\n","- $\\mathbf{\\Theta} \\in \\mathbb{R}^D$ is global visual bias vector\n","\n","Learning parameters of VBPR model can be done, similarly to BPR, via minimizing the following negative log-likelihood:\n","\n","$$ \\mathcal{L}(\\mathbf{U,V,b,E,\\Theta, P}|\\lambda) = \\sum_{(j >_i l) \\in \\mathbf{S}} \\ln (1 + \\exp\\{-(\\hat{r}_{i,j} - \\hat{r}_{i,l})\\}) + \\frac{\\lambda}{2} \\sum_{i=1}^{N} (||\\mathbf{u}_i||^2 + ||\\mathbf{p}_i||^2) + \\frac{\\lambda}{2} \\sum_{j=1}^{M} (b_j + ||\\mathbf{v}_j||^2) + \\frac{\\lambda}{2} ||\\mathbf{\\Theta}||^2 + \\frac{\\lambda}{2} ||\\mathbf{E}||^2_2 $$\n","\n","Noted that global bias $\\alpha$ and user bias $b_i$ do not affect the ranking of items, thus they are redundant and removed from the model parameters.\n","\n","Let's compare VBPR and BPR models with an experiment on Amazon Clothing dataset.\n"]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4LQDDr1e8j6L","outputId":"a36a30c9-3c6f-4bc8-d67b-8dcab85b9766","executionInfo":{"status":"ok","timestamp":1744701238896,"user_tz":-480,"elapsed":43843,"user":{"displayName":"Jaime Hieu Do","userId":"16382807970500480792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Optimization finished!\n","\n","TEST:\n","...\n","     |    AUC | Train (s) | Test (s)\n","---- + ------ + --------- + --------\n","VBPR | 0.7053 |   41.0477 |   1.1645\n","BPR  | 0.5992 |    0.0794 |   1.1461\n","\n"]}],"source":["K = 10\n","vbpr = VBPR(k=K, k2=K, n_epochs=50, batch_size=100, learning_rate=0.001,\n","            lambda_w=1, lambda_b=0.01, lambda_e=0.0, use_gpu=True, verbose=VERBOSE, seed=SEED)\n","bpr = BPR(k=(K * 2), max_iter=50, learning_rate=0.001, lambda_reg=0.001, verbose=VERBOSE, seed=SEED)\n","\n","ratings = amazon_clothing.load_feedback()\n","img_features, item_ids = amazon_clothing.load_visual_feature()\n","\n","item_image_modality = ImageModality(features=img_features, ids=item_ids, normalized=True)\n","\n","ratio_split = RatioSplit(\n","    data=ratings,\n","    test_size=0.2,\n","    rating_threshold=4.0,\n","    exclude_unknowns=True,\n","    item_image=item_image_modality,\n","    verbose=VERBOSE,\n","    seed=SEED,\n",")\n","\n","auc = cornac.metrics.AUC()\n","\n","cornac.Experiment(eval_method=ratio_split, models=[vbpr, bpr], metrics=[auc]).run()"]},{"cell_type":"markdown","metadata":{"id":"MR6coclHv1Do"},"source":["The results show that VBPR obtains higher performance than BPR in terms of AUC. That can be attributed to the usage of item visual features. Let's try to get some ideas on how the visual features contributing to the model recommendations."]},{"cell_type":"markdown","metadata":{"id":"JAdxJSASpswm"},"source":["### Interpreting Recommendation\n","\n","First, let's take a look at items rated by a particular user. Then we see how VBPR model recommends to the user."]},{"cell_type":"code","execution_count":13,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":129},"id":"i3FxEMm75Y_R","outputId":"d1da54bd-433c-441a-a903-98322860b92b","executionInfo":{"status":"ok","timestamp":1744701238933,"user_tz":-480,"elapsed":35,"user":{"displayName":"Jaime Hieu Do","userId":"16382807970500480792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["RATED ITEMS BY USER 111:\n"]},{"output_type":"execute_result","data":{"text/plain":["                                    URL  \\\n","0  https://www.amazon.com/dp/B00BXQF1XG   \n","1  https://www.amazon.com/dp/B002M62ES6   \n","\n","                                                                               Description  \n","0  Disguise Boy's Marvel Ultimate Spider-Man Classic Costume, 3T-4T Jumpsuit.               \n","1  Accessorize your Spider-man Costume with these Gloves. Fits most child sizes from 4-12.  "],"text/html":["\n","  <div id=\"df-48637e28-c378-4038-a85f-2a699561902f\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>URL</th>\n","      <th>Description</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>https://www.amazon.com/dp/B00BXQF1XG</td>\n","      <td>Disguise Boy's Marvel Ultimate Spider-Man Classic Costume, 3T-4T Jumpsuit.</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>https://www.amazon.com/dp/B002M62ES6</td>\n","      <td>Accessorize your Spider-man Costume with these Gloves. Fits most child sizes from 4-12.</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-48637e28-c378-4038-a85f-2a699561902f')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-48637e28-c378-4038-a85f-2a699561902f button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-7899480b-0dd9-4fb7-a891-3cc04f669746\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7899480b-0dd9-4fb7-a891-3cc04f669746')\"\n","            title=\"Suggest charts\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\n","  <script>\n","    async function quickchart(key) {\n","      const quickchartButtonEl =\n","        document.querySelector('#' + key + ' button');\n","      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n","      quickchartButtonEl.classList.add('colab-df-spinner');\n","      try {\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      } catch (error) {\n","        console.error('Error during call to suggestCharts:', error);\n","      }\n","      quickchartButtonEl.classList.remove('colab-df-spinner');\n","      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n","    }\n","    (() => {\n","      let quickchartButtonEl =\n","        document.querySelector('#df-7899480b-0dd9-4fb7-a891-3cc04f669746 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","\n","    </div>\n","  </div>\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","summary":"{\n  \"name\": \"pd\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"URL\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"https://www.amazon.com/dp/B002M62ES6\",\n          \"https://www.amazon.com/dp/B00BXQF1XG\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Description\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Accessorize your Spider-man Costume with these Gloves. Fits most child sizes from 4-12.\",\n          \"Disguise Boy's Marvel Ultimate Spider-Man Classic Costume, 3T-4T Jumpsuit.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":13}],"source":["UIDX = 111\n","\n","item_id2idx = vbpr.train_set.iid_map\n","item_idx2id = list(vbpr.train_set.item_ids)\n","img_features = vbpr.train_set.item_image.features\n","item_desc = {iid: desc for desc, iid in zip(*amazon_clothing.load_text())}\n","\n","print(f\"RATED ITEMS BY USER {UIDX}:\")\n","rated_items = np.nonzero(vbpr.train_set.matrix[UIDX].toarray())[1]\n","rated_df = defaultdict(list)\n","for i in rated_items:\n","  rated_df[\"URL\"].append(f\"https://www.amazon.com/dp/{item_idx2id[i]}\")\n","  rated_df[\"Description\"].append(item_desc[item_idx2id[i]])\n","pd.DataFrame(rated_df)"]},{"cell_type":"code","execution_count":14,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":345},"id":"5BA12M75R6W_","outputId":"8cf1a59d-e677-4197-e7dd-a924ae424715","executionInfo":{"status":"ok","timestamp":1744701238964,"user_tz":-480,"elapsed":29,"user":{"displayName":"Jaime Hieu Do","userId":"16382807970500480792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","TOP 5 RECOMMENDATIONS:\n"]},{"output_type":"execute_result","data":{"text/plain":["                                    URL     Score  CF Contribution  \\\n","0  https://www.amazon.com/dp/B002M62ES6  7.457715  1.116012          \n","1  https://www.amazon.com/dp/B007DLVLAW  7.312137  1.509102          \n","2  https://www.amazon.com/dp/B00BHPRRKS  6.589272 -0.176793          \n","3  https://www.amazon.com/dp/B0000CNR1L  6.477899  0.324168          \n","4  https://www.amazon.com/dp/B00BXQF1XG  6.426819  0.020040          \n","\n","   Visual Contribution  \\\n","0  6.341703              \n","1  5.803035              \n","2  6.766066              \n","3  6.153731              \n","4  6.406779              \n","\n","                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Description  \n","0  Accessorize your Spider-man Costume with these Gloves. Fits most child sizes from 4-12.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     \n","1  Patented elastic shoelace and fastening system. Engineered and designed to improve performance and keep laces locked and secure throughout the course of a race, game, practice, or workout.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                \n","2  Jumpsuit with muscle torso and arms attached belt.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          \n","3  No more digging around in deep toiletries bags for that pair of tweezers or spare safety pin you know is in there somewhere. Made of clear vinyl for quick viewing, the ten compartments on this unique organizer keep everything from dental floss to shaving cream readily retrievable. Organized in four zippered rows, the compartments vary in size and keep contents secure. When empty, the bag folds to a flat 10 by 14 inches. When filled, it can be laid inside a suitcase. The hook allows the bag to be hung inside a garment bag if desired, and then looped over a bathroom door or towel hook for accessibility.  Made of PVC microfiber, the bag extends to 28 by 14 inches when fully opened.--Ann Bieri  \n","4  Disguise Boy's Marvel Ultimate Spider-Man Classic Costume, 3T-4T Jumpsuit.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  "],"text/html":["\n","  <div id=\"df-2526d4d7-5ff8-4c29-9871-03296a03574e\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>URL</th>\n","      <th>Score</th>\n","      <th>CF Contribution</th>\n","      <th>Visual Contribution</th>\n","      <th>Description</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>https://www.amazon.com/dp/B002M62ES6</td>\n","      <td>7.457715</td>\n","      <td>1.116012</td>\n","      <td>6.341703</td>\n","      <td>Accessorize your Spider-man Costume with these Gloves. Fits most child sizes from 4-12.</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>https://www.amazon.com/dp/B007DLVLAW</td>\n","      <td>7.312137</td>\n","      <td>1.509102</td>\n","      <td>5.803035</td>\n","      <td>Patented elastic shoelace and fastening system. Engineered and designed to improve performance and keep laces locked and secure throughout the course of a race, game, practice, or workout.</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>https://www.amazon.com/dp/B00BHPRRKS</td>\n","      <td>6.589272</td>\n","      <td>-0.176793</td>\n","      <td>6.766066</td>\n","      <td>Jumpsuit with muscle torso and arms attached belt.</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>https://www.amazon.com/dp/B0000CNR1L</td>\n","      <td>6.477899</td>\n","      <td>0.324168</td>\n","      <td>6.153731</td>\n","      <td>No more digging around in deep toiletries bags for that pair of tweezers or spare safety pin you know is in there somewhere. Made of clear vinyl for quick viewing, the ten compartments on this unique organizer keep everything from dental floss to shaving cream readily retrievable. Organized in four zippered rows, the compartments vary in size and keep contents secure. When empty, the bag folds to a flat 10 by 14 inches. When filled, it can be laid inside a suitcase. The hook allows the bag to be hung inside a garment bag if desired, and then looped over a bathroom door or towel hook for accessibility.  Made of PVC microfiber, the bag extends to 28 by 14 inches when fully opened.--Ann Bieri</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>https://www.amazon.com/dp/B00BXQF1XG</td>\n","      <td>6.426819</td>\n","      <td>0.020040</td>\n","      <td>6.406779</td>\n","      <td>Disguise Boy's Marvel Ultimate Spider-Man Classic Costume, 3T-4T Jumpsuit.</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2526d4d7-5ff8-4c29-9871-03296a03574e')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-2526d4d7-5ff8-4c29-9871-03296a03574e button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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Engineered and designed to improve performance and keep laces locked and secure throughout the course of a race, game, practice, or workout.\",\n          \"Disguise Boy's Marvel Ultimate Spider-Man Classic Costume, 3T-4T Jumpsuit.\",\n          \"Jumpsuit with muscle torso and arms attached belt.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":14}],"source":["TOPK = 5\n","\n","recommendations, scores = vbpr.rank(UIDX)\n","\n","print(f\"\\nTOP {TOPK} RECOMMENDATIONS:\")\n","rec_df = defaultdict(list)\n","for i in recommendations[:TOPK]:\n","  cf_contrib = vbpr.beta_item[i] + np.dot(vbpr.gamma_item[i], vbpr.gamma_user[UIDX])\n","  vis_contrib = vbpr.visual_bias[i] + np.dot(vbpr.theta_item[i], vbpr.theta_user[UIDX])\n","  rec_df[\"URL\"].append(f\"https://www.amazon.com/dp/{item_idx2id[i]}\")\n","  rec_df[\"Score\"].append(scores[i])\n","  rec_df[\"CF Contribution\"].append(cf_contrib)\n","  rec_df[\"Visual Contribution\"].append(vis_contrib)\n","  rec_df[\"Description\"].append(item_desc[item_idx2id[i]])\n","pd.DataFrame(rec_df)"]},{"cell_type":"markdown","metadata":{"id":"G78hw2wTk5q-"},"source":["The recommendations (0, 2, 4), with high visual contribution, are indeed visually similar to the two items rated by the user.  That gives us some intuition on how VPBR model makes use of the item visual features in the predictions."]},{"cell_type":"markdown","metadata":{"id":"0FmiuTYiP_pB"},"source":["## References\n","\n","1.   Aggarwal, C. C. (2016). Recommender systems (Vol. 1). Cham: Springer International Publishing.\n","2.   Ma, H., Yang, H., Lyu, M. R., & King, I. (2008, October). Sorec: social recommendation using probabilistic matrix factorization. In Proceedings of the 17th ACM conference on Information and knowledge management (pp. 931-940).\n","3.   Wang, C., & Blei, D. M. (2011, August). Collaborative topic modeling for recommending scientific articles. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 448-456).   \n","4.   He, R., & McAuley, J. (2016, February). VBPR: visual bayesian personalized ranking from implicit feedback. In Thirtieth AAAI Conference on Artificial Intelligence.\n","5.   Cornac - A Comparative Framework for Multimodal Recommender Systems (https://cornac.preferred.ai/)\n","\n"]}],"metadata":{"accelerator":"GPU","colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"}},"nbformat":4,"nbformat_minor":0}