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Google Machine Learning Algorithms and SEO | PPTX
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Google Machine Learning Algorithms and SEO

Kristine Schachinger is a front-end developer and SEO consultant with extensive experience since 2005, having worked with notable companies and participating as a judge in various search awards. The document also discusses the evolution of Google’s search algorithms, particularly focusing on the role of machine learning, AI, and improvements in natural language processing through technologies like BERT and RankBrain. Key topics include the distinction between AI and machine learning, the application of algorithms in search relevance, and the implications of Google’s Helpful Content Update.

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@schachin Kristine Schachinger Kristine Schachinger • Started at a front-end dev & designer Claim to Fame – Designed Reba McEntire’s site • Started in SEO 2005 • Consultant 2009 – Present • Some sites I have worked with: GoodRx, Vice Media, Zappos, Instacart, Healthline, Jack in the Box, Discover, USA.gov (*GSA), Salon.com, Paychex,com, AndroidHeadlines.com, Patch Media etc • Judge: US Search Awards, UK Search Awards, EU Search Awards and since I said yes to all the Search Awards during the pandemic there might be more. • Specialties: Site Auditing, Site Recoveries, Technical SEO, and all the rest. • Articles in: WIX SEO, Search Engine Journal, Marketing Land, Search Engine Land, and Search Engine Watch -- among others. • Speaker: BarbadosSEO, UngaggedUK/US, State of Search. Leeds, Pubcon, SMX, RIMC, SXSWi -- and others.
@schachin Kristine Schachinger Kristine@SitesWithoutWalls.com GOOGLE, MACHINE LEARNING, & SEO. “…they are prone to hallucinating, and crucially they are incapable of justifying their utterances…”
@schachin Kristine Schachinger In the beginning, there was a … Large-Scale Hypertextual Web Search Engine
@schachin Kristine Schachinger http://infolab.stanford.edu/pub/papers/google.pdf
@schachin Kristine Schachinger The Web 1998 Would FIT on a USB
@schachin Kristine Schachinger Today in 2022… Roughly OVER half of the world's population 5.07 billion use the internet every day.
@schachin Kristine Schachinger
@schachin Kristine Schachinger Google processes TRILLIONS of queries a year & has indexed BILLIONS of Websites.
@schachin Kristine Schachinger TODAY, THERE WERE ALREADY OVER 3,567,977,254 Google searches. http://www.internetlivestats.com/google-search-statistics/
@schachin Kristine Schachinger In ONE SECOND today, there were http://www.internetlivestats.com/google-search-statistics/ http://www.internetlivestats.com/google-search-statistics/
@schachin Kristine Schachinger
@schachin Kristine Schachinger
@schachin Kristine Schachinger Enter Machine Learning
@schachin Kristine Schachinger What is Machine Learning & AI.
@schachin Kristine Schachinger Google Myth: AI, machine learning, & deep learning are all the same thing While artificial intelligence (AI) is a convenient and commonplace term, it has no widely agreed-upon technical definition. One helpful way to think about AI is as the science of making things smart. Much of the recent progress we’ve seen in AI is based on machine learning (ML), a subfield of AI where computers learn and recognize patterns from examples, rather than being programmed with specific rules. There are many different ML techniques, but deep learning is a particularly popular one right now. Deep learning is based on neural network technology, an algorithm whose architecture is inspired by the human brain and can learn to recognize pretty complex patterns, such as what “hugs” are or what a “party” looks like. https://ai.google/static/documents/exploring-6-myths.pdf
@schachin Kristine Schachinger Google Myth: AI is approaching human intelligence “While AI systems are nearing or outperforming human beings at increasingly complex tasks like generating musical melodies or playing the game of Go, they remain narrow and brittle, and lack true agency or creativity.” https://ai.google/static/documents/exploring-6-myths.pdf DALL-E image for “Robot Learning English”
@schachin Kristine Schachinger Google THERE ARE THREE PLACES GOOGLE APPLIES MACHINE LEARNING IN THE ORGANIC SEARCH ENGINE. + PRE-SCORING LANGUAGE MODELS + AD HOC POST-SCORING RANK BRAIN NEURAL MATCHING + LIVE RANKING FACTORS HELPFUL CONTENT UPDATE THE BIG DADDY! MUM IN A CLASS BY ITSELF.
@schachin Kristine Schachinger Pre-scoring algorithms. Machine Learning/AI.
@schachin Kristine Schachinger Google https://ai.google/static/documents/exploring-6-myths.pdf
@schachin Kristine Schachinger Google https://www.semanticscholar.org/paper/Rethinking-Search%3A-Making-Domain-Experts-out-of-Metzler-Tay/e3d7065d6196437259483ed8b9b70ba4b4bb76ad Pre-trained language models, by contrast, are capable of directly generating prose that may be responsive to an information need, but at present they are dilettantes rather than domain experts – they do not have a true understanding of the world, they are prone to hallucinating, and crucially they are incapable of justifying their utterances by referring to supporting documents in the corpus they were trained over.
@schachin Kristine Schachinger Google https://www.semanticscholar.org/paper/Rethinking-Search%3A-Making-Domain-Experts-out-of-Metzler-Tay/e3d7065d6196437259483ed8b9b70ba4b4bb76ad “…they do not have a true understanding of the world, they are prone to hallucinating, and crucially they are incapable of justifying their utterances by referring to supporting documents in the corpus they were trained over.” Image from DALL-E
@schachin Kristine Schachinger
@schachin Kristine Schachinger In the beginning there was… Word2Vec the Embedded Word Model Semantic Search. https://www.tensorflow.org/tutorials/representation/word2vec
@schachin Kristine Schachinger Word Embedding Vector space models (VSMs) represent (embed) words in a continuous vector space where semantically similar words are mapped to nearby points ('are embedded nearby each other'). Word2Vec https://www.tensorflow.org/tutorials/representation/word2vec
@schachin Kristine Schachinger First Steps.
@schachin Kristine Schachinger This is the Knowledge Graph Knowledge Graph
@schachin Kristine Schachinger • Words go in. • Words get assigned a mathematical address in a vector. • Similar and related words sit close to each other in the vector space. • Words are retrieved based on your query and the words it locates in the “best fit” vector. • These word “interpretations” are used to return results. Begging of Semantic Search.
@schachin Kristine Schachinger SLOW……… & Inefficient. Word2Vec
@schachin Kristine Schachinger Language Machine Learning/AI.
@schachin Kristine Schachinger Enter “BERT”
@schachin Kristine Schachinger Sesame Street and Search What is BERT? Natural Language Processing pre-training called Bidirectional Encoder Representations from Transformers, or BERT. Moving from NLU into early NLP
@schachin Kristine Schachinger Google https://searchengineland.com/how-google-uses-artificial-intelligence-in-google-search-379746 BERT. ”BERT, Bidirectional Encoder Representations from Transformers, came in 2019, it is a neural network-based technique for natural language processing pre-training. looking at the sequence of words on a page, so even seemingly unimportant words in your queries are counted for in the result.” • Year Launched: 2019 • Used For Ranking: No • Looks at the query and content language • All languages • Language Training Model: Prescoring • Very commonly used for many queries • Can you optimize for it? No
@schachin Kristine Schachinger Google https://searchengineland.com/how-google-uses-artificial-intelligence-in-google-search-379746 BERT. Example. “if you search for “can you get medicine for someone pharmacy,” BERT helps us understand that you’re trying to figure out if you can pick up medicine for someone else. Before BERT Google ignored the FOR and would mostly surfacing results about how to fill a prescription For offers context.
@schachin Kristine Schachinger https://bensen.ai/elmo-meet-bert-recent-advances-in-natural-language-embeddings/ BERT, or Bidirectional Encoder Representations from Transformers, improves upon standard Transformers by removing the unidirectionality constraint by using a masked language model (MLM) pre-training objective. The masked language model randomly masks some of the tokens from the input, and the objective is to predict the original vocabulary id of the masked word based only on its context. Unlike left-to-right language model pre-training, the MLM objective enables the representation to fuse the left and the right context, which allows us to pre-train a deep bidirectional Transformer. In addition to the masked language model, BERT uses a next sentence prediction task that jointly pre-trains text-pair representations. There are two steps in BERT: pre-training and fine-tuning. During pre-training, the model is trained on unlabeled data over different pre-training tasks. For fine-tuning, the BERT model is first initialized with the pre-trained parameters, and all of the parameters are fine-tuned using labeled data from the downstream tasks. Each downstream task has separate fine-tuned models, even though they are initialized with the same pre-trained parameters. Sesame Street and Search: BERT Definition
@schachin Kristine Schachinger Want to understand more? READ THIS. https://web.archive.org/web/20200604035315/https://mlexplained.com/2019/01/07/paper-dissected-bert-pre-training-of-deep-bidirectional-transformers-for- language-understanding-explained/
@schachin Kristine Schachinger Sesame Street and Search: Why is BERT Special? BERT can disambiguate words from the sentence and apply meaning forward and backward to those words in order to predict a masked word using those applied contexts. This is SUPER EFFICIENT!
@schachin Kristine Schachinger Because BERT can go forward and backwards to predict an unknown (masked) term and/or sentence. Also uses root words, so play for player/playing/played are the same Sesame Street and Search: Why is BERT Special? https://blog.google/products/search/search-language-understanding-bert/
@schachin Kristine Schachinger Confused yet?
@schachin Kristine Schachinger Simply put BERT or language modeling is “Language modeling – although it sounds formidable – is essentially just predicting words in a blank.”
@schachin Kristine Schachinger Why does it matter to us as SEOs? It mostly doesn’t. It was a breakthrough in Language Model Processing, because it is … + VERY Fast + Uses fewer resources + Provides better understanding of content
@schachin Kristine Schachinger • Collection of NLP Pre-Requisites https://towardsdatascience.com/a-collection-of-must-known-pre-requisite-resources- for-every-natural-language-processing-nlp-a18df7e2e027 • NLU vs NLP: What’s the Difference? https://www.bmc.com/blogs/nlu-vs-nlp-natural-language-understanding-processing/ • BERT State of Art Pre Learning AI https://ai.googleblog.com/2018/11/open-sourcing-bert-state-of-art-pre.html • GITHUB or BERT https://github.com/google-research/bert • GOOGEL PAPER: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding https://arxiv.org/abs/1810.04805 • Google Brings in BERT to Improve its Search Results https://techcrunch.com/2019/10/25/google-brings-in-bert-to-improve-its-search- results/ • Google Blog on BERT https://www.blog.google/products/search/search-language-understanding-bert/ • Future of AI in 2021 https://www.bmc.com/blogs/state-of-ai/ BERT the Deep Dive.
@schachin Kristine Schachinger Post-scoring ad hoc algorithms. Machine Learning/AI: Rank Brain
@schachin Kristine Schachinger Enter AI - LIVE. Ad Hoc Post Retrieval Document Models Rank Brain & Neural Matching
@schachin Kristine Schachinger Rank Brain. Rank Brain & Neural Matching & the Document Relevancy Model (DRAM) “Document relevance ranking, also known as adhoc retrieval is the task of ranking documents from a large collection using the query and the text of each document only.” Rank Brain.
@schachin Kristine Schachinger Rank Brain vs Neural Matching. Both are used to re-ordered the results post retrieval according to “ad hoc retrieval” methods and ”dynamic relevancy” Ranking with ONLY the document text • https://www.searchenginejournal.com/google-neural-matching/271125/ • http://www2.aueb.gr/users/ion/docs/emnlp2018.pdf
@schachin Kristine Schachinger Rank Brain
@schachin Kristine Schachinger Rank Brain. One of three (known) algorithms that uses AI on the live results Rank Brain.
@schachin Kristine Schachinger Google https://searchengineland.com/how-google-uses-artificial-intelligence-in-google-search-379746 RankBrain. Google told us RankBrain helps Google understand how words are related to concepts and can take a broad query and better define how that query relates to real-world concepts. • Year Launched: 2015 • Used For Ranking: Yes (post ad hoc) • Looks at the query and content language • Works for all languages • Very commonly used for many queries • Applied post scoring ad hoc • ”Kitchen Sink” result • Can you optimize for it? No
@schachin Kristine Schachinger Rank Brain is used for Unknown Queries where entity meanings/relationships are unclear or unknown. “KITCHEN SINK RESULTS”.
@schachin Kristine Schachinger Presence of Rank Brain means Google is confused …
@schachin Kristine Schachinger Rank Brain
@schachin Kristine Schachinger Rank Brain Also Uses Users Queries & Clicks to Help It Understand Query Intent.
@schachin Kristine Schachinger Remember this result?
@schachin Kristine Schachinger A year later … (yellow = poor intent match)
@schachin Kristine Schachinger Google + Rank Brain Also Uses Users Queries & Clicks to Help It Understand Query Intent. + GEO LOCATION
@schachin Kristine Schachinger A year later in London …
@schachin Kristine Schachinger Why location? Semantic Relevancy Sweets has no definitive entity in the US.
@schachin Kristine Schachinger • When do you see it? • Relationships are weak or unknown • -- enter Rank Brain. • Behind the scenes, data is continually fed into the machine learning process, to make results more relevant the next time. • Can be combined with other algorithms such as neural matching • No way to optimize for it • BUT you can help prevent your page from getting one of these results check your results for your queries. Make sure Google is NOT CONFUSED. Rank Brain.
@schachin Kristine Schachinger Neural Matching the other AI SEP 2018: Rank Brain has a friend now.
@schachin Kristine Schachinger Post-scoring algorithms. Machine Learning/AI: Neural Matching
@schachin Kristine Schachinger Google https://searchengineland.com/how-google-uses-artificial-intelligence-in-google-search-379746 Neural matching. Neural matching was released in 2018 - expanded to the local search results in 2019. Neural matching does specifically help Google rank search results and is part of the POST ad-hoc ranking algorithms. Links CANNOT affect this ranking sort. • Year Launched: 2018 • Used For Ranking: Yes (but post scoring) • Looks at the query and content language • Works for all languages • Very commonly used for many queries • Applied post scoring ad hoc • Can you optimize for it? Yes and No
@schachin Kristine Schachinger Google https://searchengineland.com/how-google-uses-artificial-intelligence-in-google-search-379746 Neural matching cont. “Example of how neural matching is used. Search for “insights how to manage a green” By looking at the broader representations of concepts in the query — management, leadership, personality and more — neural matching can decipher that this searcher is looking for management tips based on a popular, color-based personality guide”
@schachin Kristine Schachinger https://www.oncrawl.com/technical-seo/neural-matching-seo-content-creation-rules/ AND IT DOES NOT USE LINKS
@schachin Kristine Schachinger Neural Matching + Super Synonyms.
@schachin Kristine Schachinger Rank Brain. What is the difference between Rank Brain and Neural Matching? Rank Brain Vs Neural Matching.
@schachin Kristine Schachinger Rank Brain vs Neural Matching. RankBrain helps Google better relate pages to concepts. Neural Matching helps Google better relate words to searches. • Rank Brain = page concepts • Neural Matching = linking words to the page concepts “…neural matching, – AI method to better connect words to concepts.” - Google https://www.seroundtable.com/google-explains-neural-matching-vs-rankbrain-27300.html
@schachin Kristine Schachinger Ranking algorithms. AI Ranking Signals. Helpful Content Update.
@schachin Kristine Schachinger Google Helpful Content Update “Our classifier for this update runs continuously, allowing it to monitor newly-launched sites and existing ones. As it determines that the unhelpful content has not returned in the long-term, the classification will no longer apply. This classifier process is entirely automated, using a machine-learning model.” https://developers.google.com/search/blog/2022/08/helpful-content-update
@schachin Kristine Schachinger Google Helpful Content Update From CMSWire https://www.cmswire.com/digital-marketing/google-helpful-content-update-improves-customer-experience-and-seo-strategy/ https://developers.google.com/search/blog/2022/08/helpful-content-update
@schachin Kristine Schachinger Google Helpful Content Update https://ai.google/static/documents/exploring-6-myths.pdf
@schachin Kristine Schachinger Google Helpful Content Update Main Points • Ranking signal NOT an update • First known ranking signal that has machine learning • Continually rolling but with delays, so can take 2-3 months to catch-up with your site • Sitewide but severity based on the number of issued pages • Other factors can lessen the devaluation (like content quality on other pages) • Seems to target what Panda and Penguin did with an additional focus on the quality of “usefulness” or “helpfulness” • Is your content differentiating itself? DALL-E image for “Angry SEO”
@schachin Kristine Schachinger Google Helpful Content Update Read these documents for all the details. • Google Search's helpful content update and your website https://developers.google.com/search/updates/helpful-content-update • What creators should know about Google's August 2022 helpful content update. https://developers.google.com/search/docs/essentials • Googlee Essentials https://developers.google.com/search/blog/2022/08/helpful-content- update
@schachin Kristine Schachinger Google Myth: can’t detect AI content. AI systems can predict that content is likely created by AI. How? AI cannot create anything. It is only able to use what is knows to detect patterns and then in the case of content, use those patterns to “write content” So, AI can recognize patterns of how AI would “write” and determine a likelihood that this item is written by AI. It is not 100%, but it can be done. https://ai.google/static/documents/exploring-6-myths.pdf
@schachin Kristine Schachinger Maybe the Helpful Content should be the “Bo Hodas” Update? #BoHodasUpdate #StateofSearch2022 DALL-E image for “Helpful Content Robot”
@schachin Kristine Schachinger MUM: What is it?
@schachin Kristine Schachinger GoogleMUM (Multitask Unified Model) “…has the potential to transform how Google helps you with complex tasks. MUM uses the T5 text-to-text framework and is 1,000 times more powerful than BERT. MUM not only understands language, but also generates it.” Built on top of BERT. ____________ Possible related patent https://www.searchenginejournal.com/what-is-google-mum/407844/ https://blog.google/products/search/introducing-mum/ https://www.fastcompany.com/90681337/google-mum-search
@schachin Kristine Schachinger “A brief explanation about the significance of the multimodal model: Multimodal is a composite machine learning technique which compares and combines information from multiple sources to form a single response. The "modal" in multimodal refers to the aggregation of data within media, such as visual data from images and video, language data from text documents, and audio data from music and sound recordings. Modalities are incorporated into the training dataset for machine learning models. Multimodal sentiment analysis, for example, can inspect various combinations of text, audio and visual data to assess the sentiment towards an event or occurrence. With MUM, Google is treating media as modalities to improve the user experience with its search.” https://www.cmswire.com/digital-marketing/what-marketers-can-expect-from-google-mum/ GoogleMUM (Multitask Unified Model)
@schachin Kristine Schachinger “The choice of multimodal models fits Google because of the increased number of non-text based sources, such as video in the form of livestreams or similar, and audio files, as in the case of podcasts. To develop MUM, Google trained the algorithm "across 75 different languages and many different tasks at once" to refine its comprehension of information and digital details. MUM also considers knowledge across languages, comparing a query to sources that aren’t written in the user's native language to bring better information accuracy. As a result Google claims MUM is 1,000 times more powerful than BERT.” https://www.cmswire.com/digital-marketing/what-marketers-can-expect-from-google-mum/ GoogleMUM (Multitask Unified Model)
@schachin Kristine Schachinger Reid acknowledges that MUM carries its own risks. “Any time you’re training a model based on humans, if you’re not thoughtful, you’ll get the best and worst parts,” she says. She emphasizes that Google users human raters to analyze the data used to train the algorithm and then assess the results, based on extensive published guidelines. “Our raters help us understand what is high quality content, and that’s what we use as the basis,” she says. “But even after we’ve built the model, we do extensive testing, not just on the model overall, but trying to look at slices so that we can ensure that there is no bias in the system.” The importance of this step is one reason why Google isn’t deploying all its MUM-infused features today.” https://www.cmswire.com/digital-marketing/what-marketers-can-expect-from-google-mum/ GoogleMUM (Multitask Unified Model)
@schachin Kristine Schachinger MUM and COVID-19
@schachin Kristine Schachinger So should you optimize for Machine Learning algorithms and ranking signals? Do you optimize for Machine Learning?
@schachin Kristine Schachinger AI is ever-changing and unfixed. Don’t waste the time and resources on gaming it. But you can make it easier for the machine learning to get it right. Do you optimize for Machine Learning?
@schachin Kristine Schachinger So what can you do?
@schachin Kristine Schachinger “Applying” it to Your SEO.
@schachin Kristine Schachinger It is important to feed the Machine Learning the right signals and interpreters, so it does not make mistakes.
@schachin Kristine Schachinger
@schachin Kristine Schachinger DALL-E image using previous photo with text ”happy laughing baby”
@schachin Kristine Schachinger Simple answer to a very complex issue? Do your normal query research, check the SERPs for Rank Brain issues and then just write in natural and conversational language. Using specificity (topical hubs) PLUS depth & breadth to create holistic content.
@schachin Kristine Schachinger Write holistic content? Does your content have depth, breadth, & semantic relationships? Use terms that are semantically related. Image search is great for showing related terms.
@schachin Kristine Schachinger Do You Schema?
@schachin Kristine Schachinger What is Structured Data? https://developers.google.com/search/docs/guides/intro-structured-data
@schachin Kristine Schachinger What is Structured Data? Structured data for SEO purposes is on-page markup that enables search engines to better understand the information currently on your site’s web pages, and then use this information to improve search results listing by better matching user intent.
@schachin Kristine Schachinger Why Does Schema Matter?
@schachin Kristine Schachinger We can act as the interpreter and help “teach” Google what our site is about.
@schachin Kristine Schachinger Adding semantic mark-up (structured data via schema) allows us to tell Google what WE SAY our site is about and WHAT RELATIONSHIPS we define within it.
@schachin Kristine Schachinger We can act as the interpreter and help “teach” Google the context of our content.
@schachin Kristine Schachinger
@schachin Kristine Schachinger We can help give Google a clearer understanding. That helps us help Google better answer the questions users ask and to better surface our content for those users We give our data meaning Google Understands
@schachin Kristine Schachinger Ranking Without Links
@schachin Kristine Schachinger And Use Well Formed Text.
@schachin Kristine Schachinger Well Formed Text & Parsey McParseFace. http://www.kurzweilai.net/google-open-sources-natural-language-understanding-tools Ray Kurzweil on Google NLU
@schachin Kristine Schachinger Questions = Well Formed Text https://ai.google/research/pubs/pub47323 “Understanding natural language queries is fundamental to many practical NLP systems. Often, such systems comprise of a brittle processing pipeline, that is not robust to "word salad" text ubiquitously issued by users. However, if a query resembles a grammatical and well-formed question, such a pipeline is able to perform more accurate interpretation, thus reducing downstream compounding errors.”
@schachin Kristine Schachinger Google is an Answer Engine. PARTIALLY BECAUSE that is how MACHINE LEARNING works best.
@schachin Kristine Schachinger THINK in Intent, Query Terms, & Context.
@schachin Kristine Schachinger And Questions!
@schachin Kristine Schachinger Takeaways. • Think Search Queries NOT Simple Keywords • Write in natural, conversational language • Write using holistic content • Focus on depth and breadth with related terms • Add Structured Data • Use well formed text (ie questions) when you can. Takeaways.
@schachin Kristine Schachinger Slow and Efficient Snail. DALL-E GETS IT RIGHT!
@schachin Kristine Schachinger
@schachin Kristine Schachinger
@schachin Kristine Schachinger Kristine@SitesWithoutWalls.com GOOGLE, MACHINE LEARNING, & SEO. “…they are prone to hallucinating, and crucially they are incapable of justifying their utterances…”
@schachin Kristine Schachinger Kristine Schachinger Contacts • Twitter &Insta: @schachin • LinkedIn: https://www.linkedin.com/in/kschachinger-seo • Email: Kristine@SitesWithoutWalls.com

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