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1 1 HOW SEARCH ENGINES REALLY WORK IN 2023

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4 Salutations! I’m Mike King (@iPullRank)

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5 5 Disclaimer: Just because something is in a patent, or a whitepaper does not mean that Google uses it…but it probably does.

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6 6 Disclaimer: Correlation is not causation… but it probably is.

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7 So, I Have a Book Coming Soon The Science of SEO: Decoding Search Engine Algorithms. This is the cover that my publisher sent me. I’m not a fan, but we’ll see what happens. Anyway, you can preorder it wherever books are sold. Here’s the Amazon link. https://amzn.to/3T9qkYN

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8 8 Thesis: Search engines are not magic. You can deeply understand them if you learn more about Information Retrieval and pay close attention to engineering research.

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9 9 If you pay enough attention, they are telling you everything you want to know about how Google works.

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10 A Selective History of Information Retrieval and Search Engines

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11 Meet Mortimer Taube Taube invented the “Uniterm Indexing System” in 1951 because he felt the Dewey Decimal System could not keep up with the pace of information after the war. This is the basis of what is called an Inverted Index, the data structure behind what we think of as an “index” in search engines.

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12 Meet Hans Peter Luhn This guy invented the concept of Term Frequency in his paper “The Automatic Creation of Literature Abstracts.” He also invented hashmaps, but we’ll talk about that later.

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13 Meet Sparck Jones Sparck Jones is considered the godmother of IR. She contributed to information retrieval in many ways, but she is best known for inventing Inverse Document Frequency in the 70’s.

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14 14 The Two Concepts Together Yielded TF*IDF

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15 Meet Gerard Salton Gerard Salton is the godfather of Information Retrieval. Much of how search engines of all kinds work is based on methods that he and his team invented.

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16 16 Gerard Salton invented the Vector Space Model In the vector-space model, documents and queries are converted to vector representations and plotted in multi-dimensional space. The query and document vectors are then compared based on cosine similarity and the ones that are closest to the query are the most relevant. The main takeaway here is that relevance is a quantitative value. This is perhaps the most important concept to understand about how search works.

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17 17 Disclaimer: Correlation is not causation… but it probably is. You are here

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18 18 He’s also Responsible for the SMART Evaluation Model

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19 19 Amit Singhal was a graduate student that studied directly under Gerard Salton.

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20 20 Amit Singhal Rewrote Google Search in 2001 And he was nice enough to tell us exactly how he did it. http://singhal.info/ieee2001.pdf

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21 21 Around this Time, Google’s Scoring Functions were Mostly just PageRank + BM25(F)

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22 22 Meet Brian Pinkerton Brian built the first commercially available web-scale search engine based on a crawler called WebCrawler in 1994. He wrote about it extensively for his PhD thesis. http://www.thinkpink.com/bp/Thesis/ Thesis.pdf

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23 Every Search Engine is Based on WebCrawler Every search engine can trace its roots back to WebCrawler to some degree. In fact, Lycos, AltaVista, and Google all reference it in their early papers and patents. You know why page titles have been so important for so long? Early search engines only indexed page titles.

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24 24 Google Copied a Lot from AltaVista

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25 25 …including Penalizing Websites

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26 26 Many Googlers, of course, came from AltaVista

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27 27 Meet Jeff Dean This is Jeff Dean. He’s had an engineering hand in many of Google’s most important innovations ever.

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28 28 Jeff Talks About How He Ended Up at Google from AltaVista https://www.quora.com/What-was-it-like-to-work-on-the-AltaVista-team-in-the- 90s?ch=10&oid=960520&share=21e5a871&srid=uHsr&target_type=question

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29 29 The Guy is the Chuck Norris of Computer Science

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30 30 Fun Fact I’m the only SEO that Jeff follows, so by the principles of PageRank, I’m the greatest SEO of all time. �

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31 Tweets is Watching The SVP that runs Search at Google is also following me, so if anything happens to me…

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33 33 Google Operates as a Shared Environment All the software across eco can be installed on any machine and any process can be run on any machine. For example, a crawler could also on a machine that is managing rendering or processing or anything else.

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34 34 Fun Fact: Penguin was built on top of Panda Panda was a group-specific modification factor that was computed as a function of: The number of independent links divided by the number of reference queries. Penguin built on top of that quality score and applied it to links.

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35 Our Understanding of Search Engines Is Out of Date

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36 36 At a Base Level, This is What all Search Engines Do Fundamentally, this is the basis of how search engines function. Google has developed many layers on top of this, but this is the core of what they all do.

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37 37 Google’s High-Level Pipeline Abstraction

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38 38 We know this, but there is a single set of innovations that sped Google past the SEO community.

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39 39 Lexical Search vs Semantic Search are the Two Primary Search Models What we as the SEO community do not have a strong enough handle on is that most of what Google’s doing is on the semantic side and that has all improved dramatically over the last 10 years based on machine learning.

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40 40 Vector Space Model Again Let’s go back to the vector space model again. This model is a lot stronger in the neural network environment because Google can capture more meaning in the vector representations.

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41 41 Words are Converted to Multi-dimensional Coordinates in Vector Space

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42 42 This Allows for Mathematical Operations Comparisons of content and keywords become linear algebraic operations.

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43 43 Relevance is a Function of Cosine Similarity When we talk about relevance, it’s the question of similar is determined by how similar the vectors are between documents and queries. This is a quantitative measure, not the qualitative idea of how we typically think of relevance.

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44 44 TF-IDF Vectors The vectors in the vector space model were built from TF-IDF. These were simplistic based on the Bag-of-Words model and they did not do much to encapsulate meaning.

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45 Word2Vec Gave Us Embeddings Word2Vec was an innovation led by Tomas Milosevic and Jeff Dean that yielded an improvement in natural language understanding by using neural networks to compute word vectors. These were better at capturing meaning. Many follow-on innovations like Sentence2Vec and Doc2Vec would follow.

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46 46 We Went from Sparse Embeddings to Dense Embeddings

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48 48 Word2Vec Captured Relationship, but Not Context – BERT Captures Context

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49 49 BERT Yields Embeddings with Higher Dimensionality and Information Capture

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50 50 Source: https://cloud.google.com/blog/topics/developers-practitioners/find-anything-blazingly- fast-googles-vector-search-technology

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51 Dense Retrieval You remember “passage ranking?” This is built on the concept of dense retrieval wherein there are more embeddings representing more of the query and the document to uncover deeper meaning.

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52 52 Dense Retrieval is Scoring down to the Sentence Level

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53 53 Introducing Google’s Version of Dense Retrieval Google introduces the idea of “aspect embeddings” which is series of embeddings that represent the full elements of both the query and the document and give stronger access to deeper information.

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54 54 Dense Representations for Entities Google has improved its entity resolution using embeddings giving them stronger access to information in documents.

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55 55 Embeddings keep getting better at capturing meaning while SEO tools still operate on the Lexical Search model

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57 57 I Feel Like My Page is More Relevant to [Enterprise SEO]

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58 58 Relevance isn’t Qualitative to Google.

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60 60 See! My page is more relevant, but it’s not ranking as well.

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61 61 https://ipullrank.com/tools/orbitwise

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62 How Crawling Works

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63 63 Crawling in the High-Level Pipeline Abstraction You are here

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64 64 How Google Crawls the Web Most of the magic happens in the URL manager. The crawler simply accesses a page and extracts it. The processing pipeline handles most of the actual parsing. Source: Distributed Crawling of Hyperlinked Documents https://patents.google.com/patent/U S8812478B1/en

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65 65 Google Doesn’t Crawl Link to Link; The Crawl Based on a URL Queue

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66 66 Crawling is Stateless Googlebot does not hold a “state.” Although it has the capabilities to, it does not maintain cookies, fill out forms, or make POST requests. Every page it looks at is as though it turned logged on to the web for the first time and

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67 67 Google Crawls with a Very Tall Viewport As we know Googlebot is crawling mobile-first primarily, but they have limits of what they will see based on infinite scroll.

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68 68 Crawl Models Typical IR models are breadth-first (whole level is reviewed) or depth-first (last node every path before moving on). Google uses a “best-first” model following PageRank Depth-first Breadth-first

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69 69 Where Does the “Search Engines Only Crawl 5 Levels Deep” Come From? A paper by IR legend and Yahoo researcher Ricardo Baeza-Yates entitled “Crawling the Infinite Web” identified that crawling only five levels deep is enough to get the most valuable content on the web. https://chato.cl/papers/baeza04_cra wling_infinite_web.pdf

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70 70 Crawl Frequency Estimation Google would love to use your dates from Schema and your lastmod from your sitemap, but they can’t trust them. So, keep every version of your content that they crawl and they make determinations on how frequently pages change to decide how often to crawl the page.

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71 71 They May Stop Crawling Based on URL Patterns If Google believes that the URL pattern is going to yield less value if they crawl the page, they will stop crawling all URLs that fit that pattern.

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72 72 How XML Sitemaps Come Into Play Google downloads XML sitemaps regularly from a separate crawler to update their “per site” database. That database informs the list of URLs that go to the scheduler and it treats “differential sitemaps” with higher priority. There’s also a secondary crawler system for URLs in XML Sitemaps.

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73 73 That’s why this works so well.

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74 74 The Generative AI Hack to Increase Crawl for a Page A good way to improve crawl is by updating your pages regularly. An automated way to make it change is by putting a NLG summary at the top of the page and updating it frequently.

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75 75 Gary says there’s no crawl budget only crawl rate limit and crawl demand.

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76 76 Crawl Rate Limiting How often Google crawls is a function of how much load a host can handle. Increase the capacity and they will crawl more.

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77 Crawl Demand This is an area where social signals used to play a heavy factor, but crawl demand is mostly a function of PageRank.

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78 78 On Crawl Budget = Server Response Time x Time / Error Rate = TTFB x Duration / %Server Error = (Avg. TTFB x Duration / %Server Error) * (CTR x Average Time between page updates) = (Avg # of Crawled URLs x Frequency) / Time @JoriFord

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79 79 How Google Handles Pages that Don’t Change Pages that have either explicitly or implicitly indicated that they don’t change (304 response code) are basically put on a timeout for a while and Google will reuse what it has in the index. That cache expiry refreshes on a set interval.

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80 80 What About IndexNow? I don’t see Google joining this initiative because of the cross-search engine URL submission requirement. I could imagine them coming up with their own version of the spec though.

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81 81 The Best Things to Do to Get More Crawl Activity Load Balance – Route Googlebot to its own autoscaling instances by IP Submit Differential Sitemaps Update your pages regularly Align lastmod with structured data date and on-page date Make sure your robots.txt never returns a 500 Track your crawl budget metrics

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82 How Indexing Works

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83 83 Indexing in the High-Level Pipeline Abstraction You are here

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84 84 Back in the day Google Only Indexed the first 100kb of the page. Now they do 15MB

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85 85 Estimates Suggest Google Indexes 0.03% of the Web; 60% of the Web is Duplicate

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86 86 Documents are Parsed and Stored in an Inverted Index An inverted index is like an index in a book where each word is mapped to documents that it appears in.

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87 87 Phrase-Based Indexing was a key Google Innovation Before Anna Paterson led the phrase- based indexing initiative, search engines built inverted indexes on single phrases and then built posting lists at the intersections of phrases in queries. Phrase-based indexing upended this and introduced phrase co-occurrence and predictive modeling based on those phrases.

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88 88 Google Saves Versions of Your Pages Forever in the Document Server There are a variety of operations that Google does based on your content over time. So they have cached versions from the first time a page appeared.

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89 89 This is Replicated Many Many Times Over

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90 90 Crawl Tiering-based on Update The index is stored in multiple tiers across many machines and split into three dimensions based on how important the page is. Super important and regularly accessed pages are stored in memory. Pages of medium importance stored on solid state drives for fast reads. Pages that are not so important are stored in standard HDDs since they are cheap and don’t need to be fast. Distributed Crawling of Hyperlinked Documents https://patents.google.com/patent/U S8812478B1/en

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91 91 Deduplication and Canonicalization Deduplication and canonicalization are handled through a series of fingerprints and comparison. There many signals that inform this process such as links, redirects, alternates, etc. Google uses a machine learning classifier to make the final canonical determination.

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92 92 The Best Things to Improve Indexing Limit Duplication with More Unique content per page Limit your cannibalization through your anchor text Update your pages regularly

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93 How Rendering Works

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94 94 Indexing in the High-Level Pipeline Abstraction You are here

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95 95 Why We Need Rendering Historically Google could not see what happens here.

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96 96 The Web Rendering System Closes the Gap The Web Rendering System uses a modified version of headless Chromium to render pages. It has different behaviors than a users browser like how it handles random, dates, and service workers. It doesn’t paint pixels because there’s no reason to, but it will stop executing if a process takes up too much CPU.

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97 97 Rendering is Separate Because It’s Computationally Expensive The WRS is not going to render every page unless it believes its worthwhile.

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98 98 Websites Have Many Options for Rendering These Days Google handles SSR the best, obviously, but they can access your content with any of these models.

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99 99 The Best Things to Improve Rendering SSR, if you can Make your pages worth rendering if you can’t Monitor your crawl volume vs CPU usage

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10 0 How Processing Works

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10 1 10 1 Processing in the High-Level Pipeline Abstraction You are here

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10 2 10 2 Processing is Where the Magic Happens

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10 3 10 3 Standard NLP Pipeline

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10 4 10 4 There are Query-Dependent and Query-independent Scores

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10 5 105 Embeddings have disrupted our understanding of every part of the processing pipeline.

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10 6 Websites as Vectors Just as there are representations of pages as embeddings, there are vectors representing websites and authors.

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10 7 Author Vectors Similarly, Google has Author Vectors wherein they are able to identify an author and the subject matter that they discuss. This allows them to fingerprint an author and their expertise.

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10 8 Build Your Links Contextually If you’re still building links, it’s very likely that they have ramped up the capabilities around relevance between pages for links. They are likely discounting pages that are not close relevance matches anymore.

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10 9 How Searching Works

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11 0 11 0 Google Is Now a Series of Over 200 Microservices Running in Parallel

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11 1 Misspellings are Fixed Of course.

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11 2 11 2 Queries are Expanded and Substituted Based on Entities and Synonymy

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11 4 11 4 Expansions Are Scored The different versions of the query are scored and they may be ran in parallel and then the results are scored and then they return the best set.

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11 5 11 5 There is More Happening Behind the Scenes With Entities

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11 6 11 6 Here’s an example [The Rock]

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11 7 11 7 Here’s an example [The Rock] It’s also relevant to a movie called “The Rock”

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11 8 11 8 [The Rock imdb] Google’s not sure what you mean here, so it’s showing both.

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11 9 11 9 [The Rock] is expanded to [The Rock actor] in the background

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12 0 12 0 There is More Happening Behind the Scenes With Entities

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12 1 12 1 That’s How Results Like This Happen

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12 2 12 2 Neural Matching to Determine the Meaning of the Query Again, with the embeddings!

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12 3 How Ranking Works

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12 4 12 4 Document Scoring Simplified Content Factor Content Factor Speed Factor Link Factor Link Factor Document Score + + + + = HOW SEARCH ENGINES REALLY WORK IN 2023

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12 5 12 5 Each Component to the Equation Has a Weight Content Factor Content Factor Speed Factor Link Factor Link Factor Document Score a b c d e + + + + = HOW SEARCH ENGINES REALLY WORK IN 2023

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12 6 12 6 The Weights May Look Like This Content Factor Content Factor Speed Factor Link Factor Link Factor Document Score 3 6 1 2 2 + + + + = HOW SEARCH ENGINES REALLY WORK IN 2023

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12 7 12 7 This is What Marketers Do 5 2 4 95 74 Content Factor Content Factor Speed Factor Link Factor Link Factor 369 3 6 1 2 2 + + + + = HOW SEARCH ENGINES REALLY WORK IN 2023

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12 8 12 8 So, Then, Google Turns Down the Weights on Links 5 2 4 95 74 Content Factor Content Factor Speed Factor Link Factor Link Factor 55.49 3 6 1 .25 .01 + + + + = HOW SEARCH ENGINES REALLY WORK IN 2023

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12 9 12 9 Google Understands Queries by Breaking them Into Entities Leveraging Entities Allows Queries to be Expanded and Related Entities and Attributes to be Discovered

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13 0 Google’s Scoring Functions There’s more than one scoring function. Google scores content and links a variety of different ways and then chooses the best results. There is not just one “algorithm.” This is why different queries seem to value signals differently. HOW SEARCH ENGINES REALLY WORK IN 2023

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13 1 Post-retrieval Adjustments In addition to their being multiple scoring functions with different results to choose from, Google may make further re-ranking adjustments based on any number of features and factors. So, really, anything could happen in the SERPs.

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13 2 13 2 When Amit Singhal ran Google Search he was famously against using machine learning in rankings.

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13 4 13 4 Machine Learning for Search Rankings Has Been Around For a Long Time

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13 6 13 6 John Giannandrea from Google Brain took over and certainly did not have that bias.

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13 7 13 7 Learning to Rank Learning to Rank is using supervised machine learning for information retrieval systems.

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13 8 13 8 Learning to Rank Requires One of Two Things Human reviewed quality scores Implicit User feedback

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13 9 13 9 Google Has the Quality Rater Program The Quality Ratings are not just for evaluation. They act as the feature engineered data that trains the learning to rank models.

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14 0 140 Google Obviously Has Query and Click Logs

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14 1 14 1 Enter TensorFlow Rankings

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14 2 14 2 It’s Open Source!

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14 3 14 3 We Keep Hearing How they Don’t Use Clicks

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14 4 14 4 At Best, This is a Lie by Omission

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14 5 14 5 Fascinating Blog post from the Google Cloud team…

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14 6 14 6 Here’s Google Telling On Itself

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14 7 147 The inputs that we control have not changed, but our understanding of what Google is doing with them needs to.

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14 8 What about the new Search Generative Experience (SGE)? HOW SEARCH ENGINES REALLY WORK IN 2023

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14 9 At I/O Google Announced a Dramatic Change to Search The experimental “Search Generative Experience” brings generative AI to the SERPs and significantly changes Google’s UX.

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15 0 15 0 Queries are Longer and the Featured Snippet is Bigger 1. The query is more natural language and no longer Orwellian Newspeak. It can be much longer than the 32 words that is has been historically in order 2. The Featured Snippet has become the “AI snapshot” which takes 3 results and builds a summary. 3. Users can also ask follow up questions in conversational mode. 3 2 1

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15 1 15 1 This is Called “Retrieval Augmented Generation” Neeva, Bing, and now Google’s Search Generative Experience all use pull documents based on search queries and feed them to a language model to generate a response.

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15 2 15 2 Google’s Version of this is called Retrieval-Augmented Language Model Pre-Training (REALM )

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15 3 15 3 SGE is built from REALM + PaLM 2 and MUM MUM is the Multitask Unified Model that Google announced in 2021 as way to do retrieval augmented generation. PaLM 2 is their latest state of the art large language model.

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15 4 15 4 If You Want More Technical Detail Check Out This Paper https://arxiv.org/pdf/2002.08909.pdf

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15 5 It’s Experimental because it’s Error-prone Bing and ChatGPT lit a competitive fire under Google, but they have been working on these technologies for years. They were slow to release because of the various reasons that LLMs are likely to return disinformation.

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15 6 15 6 The Experience May Also Pollute Search Quality The experience of a response from Google suggests that there is a person giving the response. The generative text may also conflict with other aspects returned in search.

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15 7 157 Sounds cool, but how is it going to affect what we do?

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15 8 The Search Demand Curve will Shift With the change in the level of natural language query that Google can support, we’re going to see a lot less head terms and a lot more long tail term. Going down Going up

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15 9 15 9 The CTR Model Will Change With the search results being pushed down by the AI snapshot experience, what is considered #1 will change. We should also expect that any organic result will be clicked less and the standard organic will drop dramatically. However, this will likely yield query displacement.

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16 0 Rank Tracking Will Be More Complex As an industry, we’ll need to decide what is considered the #1 result. Based on this screenshot positions 1- 3 are now the citations for the AI snapshot and #4 is below it. However, the AI snapshot loads on the client side, so rank tracking tools will need to change their approach.

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16 1 161 None of this changes what we do tactically, but it may change what we do strategically.

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16 2 The future of Content and Links Or how is generative AI going to change all of this for us? HOW SEARCH ENGINES REALLY WORK IN 2023

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16 3 163 Disclaimer: Correlation is not causation… but it probably is. You are here

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16 4 164 We’ve Evolved Beyond Word Counts.

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16 5 16 5 One of Singhal’s Early Innovations was Doc Length Normalization Google has always had the idea of making sure content length isn’t an overpowering factor. Amit Singhal recognized longer documents inherently outperform shorter ones in retrieval tasks, so it’s always been a fundamental thing that Google looked at.

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16 6 Marketers Are Just Copying… People are skipping the step in the Skyscraper technique wherein they’re are supposed to create “better” content.

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16 7 16 7 This is What a Lot of Us Are Doing Now HOW SEARCH ENGINES REALLY WORK IN 2023

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16 8 16 8 Here’s Google Telling On Itself

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16 9 16 9 Here’s Google Telling On Itself https://ipullrank.com/ai-seo-guide

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17 0 170 We need to evolve beyond what is basically complex “keyword density.”

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17 1 17 1 Soon, Everyone will be able to Generate Perfectly Optimized Content

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17 2 In Fact, Kristin @ Fractl Built It Kristin built a tool that allows someone to put in a keyword or a topic and it will generate robust content based on what is currently ranking. https://www.frac.tl/interactives/long- form-article-generator/

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17 4 17 4 This is Where Information Gain Comes Into Play Conceptually, as it relates to search engines, Information Gain is the measure of how much unique information a given document adds to the ranking set of documents. In other words, what are you talking about that your competitors are not?

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17 5 Google’s Information Gain Patent Google’s patent indicates that they are specifically scoring for documents that feature net new information over other documents on the same topic.

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17 6 17 6 Information Gain is Best Driven by Looking at Across the Entity Graph •Thus far, there is a very limited set of tools in the SEO space that are specifically looking at entities and their relationships. A non-SEO tool called EntiTree visualizes related entities from Wikidata. https://www.entitree.com/ Using this will give you insights into what entities are being considered for your target entity.

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17 7 177 Right now, most tools are just showing you how to be a copycat.

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17 8 17 8 It’s Not Exactly Clear What SEO Tools Are Looking At These seem to be topics, but are they entities?

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17 9 17 9 Some Tools Are Looking Vertically at a SERP for Term Co-occurence •While it’s possible that it may yield the same or similar results, tools like this are not looking across relationships of entities.

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18 0 18 0 Other Tools are Mapping Topical Clusters •While this approach captures more breadth as it relates to the topic, it is not the same as reviewing entities.

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18 1 18 1 Review the Details of the Entity in Wikidata

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18 2 18 2 Review the Features of the Entity and Talk About it In Your Content •Ultimately, the process is the same. Work the discussion entities, their attributes and related entities into your content in all the relevant places in your content.

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18 3 183 If it’s not an entity that Google recognizes, it’s not worth optimizing for.

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18 4 18 4 Get Entity SEO Tools Into the Workflow HOW SEARCH ENGINES REALLY WORK IN 2023

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18 5 18 5 Quick Tool: Reviewing Entity Salience HOW SEARCH ENGINES REALLY WORK IN 2023 I whipped up a quick tool in Colab where you can see how entities are appearing in your own content. You can put text, upload a file, or select URL. Compare the usage of entities in your content with your competitors. https://colab.research.google.com/drive/18QXrdAPoKhUl76gGzuxk_vDiUqMeRyqx?usp=sharing

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18 6 18 6 Context-Limited Generative AI is a Huge Opportunity for SEO

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18 7 18 7 Build a bot based on your own content

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18 9 18 9 If you’re using ChatGPT, you need AIPRM for prompt management. https://www.aiprm.com

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19 1 191 If your prompt is just one sentence, don’t be surprised when you get garbage back.

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19 2 19 2 Remember You Need to Build Around a Content Strategy Read more about this approach at https://ipullrank.com/generative-ai-content-strategy

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19 3 193 Aight, that’s enough.

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19 4 Signing Off (for the Last Time) Roll the Credits

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19 5 195 We are firmly in a semantic search environment. We need to stop operating from the lexical model.

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19 6 19 6 The Things You Should Do Don’t Use qualitative measures in the places where Google is using quantitative measures Use tools that calculate embeddings Improve the management of your XML sitemaps Leverage generative AI to scale content optimization Build links contextually Start, actually using entities

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19 8 19 8 Check Out FridAI It’s on AIPRM.com.

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19 9 https://ipullrank.com/resources/seo-weekly

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Mike King Founder / CEO @iPullRank Thank You | Q&A [email protected] Award Winning, #GirlDad Featured by Download the AI Guide: https://ipullrank.com/ai-seo-guide Use Orbitwise: https://ipullrank.com/tools/orbitwise