of Tiktok users perform searches, but the search volume around a series of broad and meaningful queries is not there to make it more than a small supplement to Google Search.
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.
of words. Whereas the semantic model captures meaning. This was the huge quantum leap behind Google’s Hummingbird update and most SEO software has been behind for over a decade. Google Shifted from Lexical to Semantic a Decade Ago
by Tomas Mikolov 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.
model used in natural language processing (NLP) that relies on self-attention mechanisms to process sequences of data simultaneously, improving efficiency and understanding in tasks like translation and text generation. Its architecture enables it to capture complex relationships within the text, making it a foundational model for many state-of-the-art NLP applications.
@dejanseo shard his research on how MixedBread’s embedding models perform better than anything else for his SEO use cases. He also talked about lowering the dimensionality and converting them to binary representations to save space.
a Page is Google is specifically vectorizing pages and sites and comparing the page embeddings to the site embeddings to see how off-topic the page is. Learn more about embeddings: https://ipullrank.com/content-relevance
embeddings with cosine similarity and clustering to examine two ways that how pages relate or don’t relate to the site average of the embeddings. Notice how my recent posts on AI related topics for SEO all have high PageSiteSimilarity whereas my post about MozCon from 2011 does not.
PyTorch, Scikit Learn, and Sentence Transfomers to compute SiteScore and a dataframe of cosine similarities and cluster based scores for all URLs crawled. https://colab.research.google.com/drive /19PJiFmv8oyjhB-jwzEK9TPlbfK-xB573 You can remove the outliers to improve your site focus score. Add this to your content audits.
that Google uses vector embeddings to determine how far off given a page is from the rest of what you talk about. This indicates that it will be challenging to go far into upper funnel content successfully without a structured expansion or without authors who have demonstrated expertise in that subject area. Encourage your authors to cultivate expertise in what they publish across the web and treat their bylines like the gold standard that it is.
your website and brand into an authority in your space and strengthen your entities in the eyes of Google. A site that focuses on a series of topics that are relevant to each other are going to benefit in rankings. Here are a few tools that can help you design and build your topic clusters systematically. Thruuu https://thruuu.com/keyword-clustering-tool Keyword Insights https://www.keywordinsights.ai/features/keyword-clustering/
while you crawl using Screaming Frog SEO Spider. Take the file to Colab and do the following things: Keyword - Landing Page Relevance Scoring Keyword Mapping Link Building Target Identification Redirect Mapping Internal Link Mapping https://ipullrank.com/vector-embedding s-is-all-you-need You can also work with your language model to combine crawl data with SERP data and do things like information gain calculations.
Full Display Lately Through a combination of what’s come out of Google’s DOJ antitrust trial and the Google API documentation leak, we have a much clearer picture of how Google actually functions.
Powerful Than Any Other Search Engine The court opinion in the DoJ Antitrust trial, Google’s leaked documents, and Google’s own internal documentation all support the fact that click behavior is what makes Google perform the way that it does.
performance for every position in the SERP. The user behavior signals collected reinforce what should rank and demote what doesn’t perform just like a social media channel. The best way to scale this is by generating highly-relevant content with a strong user experience.
content so it is easier to consume and it will yield better performance metrics. https://moz.com/blog/10-super-easy-seo-copywriting-tips-for-link-building
Quality Indexing Tier Impacts Link Value A metric called sourceType that shows a loose relationship between the where a page is indexed and how valuable it is. For quick background, Google’s index is stratified into tiers where the most important, regularly updated, and accessed content is stored in flash memory. Less important content is stored on solid state drives, and irregularly updated content is stored on standard hard drives. The higher the tier, the more valuable the link. Pages that are considered “fresh” are also considered high quality. Suffice it to say, you want your links to come from pages that either fresh or are otherwise featured in the top tier. Get links from pages that live in the higher tier by modeling a composite score based on data that is available.
and Uses the Change History Google’s file system is capable of storing versions of pages over time similar to the Wayback Machine. My understanding of this is that Google keeps what it has indexed forever. This is one of the reasons you can’t simply redirect a page to an irrelevant target and expect the link equity to flow. The docs reinforce this idea implying that they keep all the changes they’ve ever seen for the page. You’re not going to get away with things by simply changing your pages once.
as much, but indexing has gotten a lot harder since the Helpful Content update. You’ll see a lot more pages in the “Discovered - currently not indexed” and “Crawled - currently not indexed” than you did previously because the bar is higher for what Google deems worth capturing from the web.
Gain 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?
indication of what this means, but the description mentions “human-labeled documents” versus “automatically labeled annotations.” I wonder if this is a function of quality ratings, but Google says quality ratings don’t impact rankings. So, we may never know. 🤔
Dabbas created a python script and tool that uses the Helpful Content Recommendations to show a proof of concept way to analyze your articles. We’d use the Search Quality Rater Guidelines which serve as the Golden Document standard. Code: https://blog.adver.tools/posts/llm-content-evaluation/ Tools: https://adver.tools/llm-content-evaluation/
great place to work through whether your content should be pruned or not. https://www.aleydasolis.com/en/crawli ng-mondays/how-to-prune-your-website- content-in-an-seo-process-crawlingmon days-16th-episode/
rapidly changing organism. Google always wants the most relevant content, with the best user experience, and most authority. Unless you stay on top of these measures, you will see traffic fall off over time. Measuring this content decay is as simple comparing page performance period over period in analytics or GSC. Just knowing content has decayed is not enough to be strategic.
- 100: High Priority for Optimization 60 - 79: Moderate Priority for Optimization 40 - 59: Selective Optimization 20 - 39: Low Priority for Optimization 0 - 19: Minimal Benefit from Optimization If you want quick and dirty, you can prune everything below a 40 that is not driving significant traffic.
from GSC Google Search Console is a great source to spot Content Decay by comparing the last three months year over year. Filter for those pages where the Click Difference is negative (smaller than 0) then export.
Action Each URL is marked as Keep, Revise, Kill or Review based on the keyword opportunities available and the effort required to capitalize on them. Sorting the URLs marked as “Revise” by Aggregated SV and CPR will give you the best opportunities first.
for channels outside of Organic Search. So, killing it is about changing Google’s experience of your website to improve its relevance and reinforce its topical clusters. The best approach is to noindex the pages themselves, nofollow the links pointing to them, and submit an XML sitemap of all the pages that have changed. This will yield the quickest recrawling and reconsideration of the content.
across the topic cluster Use co-occurring keywords and entities in your content Add unique perspectives that can’t be found on other ranking pages Answer common questions Answer the People Also Ask Questions Restructure your content using headings relevant to the above Add relevant Structured markup Expand on previous explanations Add authorship Update the dates Make sure the needs of your audiences are accounted for Add to an XML sitemap of only updated pages
that has a low content potential rating and a minimum of 500 in monthly search volume as “Review” because they may be long tail opportunities that are valuable to the business. You should take a look at the content you have for that landing page and determine if you think the effort is worthwhile.
Language Model is called “Retrieval Augmented Generation” Neeva (RIP), 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. This concept was developed by the Facebook AI Research (FAIR) team.
called Retrieval-Augmented Language Model Pre-Training (REALM) from 2021 REALM identifies full documents, finds the most relevant passages in each, and returns the single most relevant one for information extraction.
(RETRO) DeepMind's RETRO (Retrieval-Enhanced Transformer) is a language model that combines a large text database with a transformer architecture to improve performance and reduce the number of parameters required. RETRO is able to achieve comparable performance to state-of-the-art language models such as GPT-3 and Jurassic-1, while using 25x fewer parameters.
Revision (RARR) RARR does not generate text from scratch. Instead, it retrieves a set of candidate passages from a corpus and then reranks them to select the best passage for the given task.
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 (released) state of the art large language model. The functionality from REALM, RETRO, and RARR is also rolled into this.
content 1. Generative AI is not the end-all-be-all solution. It is not the replacement for a content strategy or your content team. 2. Generative AI for content creation should be a force multiplier to be utilized to improve workflow and augment strategy. 3. You should consider generative AI content for awareness efforts, but continue to leverage subject matter experts for lower funnel content.
Llama Index sitemap_url = "[SITEMAP URL]" sitemap = adv.sitemap_to_df(sitemap_url) urls_to_crawl = sitemap['loc'].tolist() ... # Make an index from your documents index = VectorStoreIndex.from_documents(documents) # Setup your index for citations query_engine = CitationQueryEngine.from_args( index, # indicate how many document chunks it should return similarity_top_k=5, # here we can control how granular citation sources are, the default is 512 citation_chunk_size=155, ) response = query_engine.query("YOUR PROMPT HERE")
a series of generative AI use cases that work well for your situation. Briefing & Business Cases Content Analysis First-pass Brand Review First-pass Legal Review Content First Draft Keyword Insertion Structured Data Generation Link Identification & Insertion Generating Voiceovers Generating Images Generating Videos Writing Code
your language model what you want the code to do and it will handle the rest. If it doesn’t work, just describe what went wrong or paste the error and it will fix it for you. In this example my prompt is: {write python code for colab that takes a csv file of keywords and using bertopic with the chatgpt to compute the natural language topics for each row.}
KG-enhanced LLMs - Language Model uses KG during pre-training and inference 2. LLM-augmented KGs - LLMs do reasoning and completion on KG data 3. Synergized LLMs + KGs - Multilayer system using both at the same time https://arxiv.org/pdf/2306.08302.pdf Source: Unifying Large Language Models and Knowledge Graphs: A Roadmap
Manage expectations on the impact 2. Understand the keywords under threat 3. Re-prioritize your focus to keywords that are not under threat 4. Optimize the passages for the keywords you want to save
GEO team also shared the ChatGPT prompts that help them improve their visibility. You can augment them and put them to work right away. https://github.com/GEO-optim/GEO/blo b/main/src/geo_functions.py
to win Google is still the primary show in town Relevance is a quantitative measure GenAI works on the same math as search engines Focus on making your chunks for relevant to rank in GenAI Search Improve UX to drive more long clicks Focus on content your audience wants, prune what they don’t Use RAG to generate content with AI
your SEO: [email protected] Thank You | Q&A Award Winning, #GirlDad Featured by Download the Slides: https://speakerdeck.com/ipullrank Mike King Chief Executive Officer @iPullRank
ipullrank
dougneiner
uxyall
kristinabergwall1
lindahogenes
eileencodes
jnunemaker
akashhashmi
mojombo
62gerente
bcantrill
addyosmani
