What Is Cosine Similarity?
Cosine similarity is a way of measuring how similar two pieces of text are, based on their meaning rather than their exact words.
Here is the plain-English version. Imagine every piece of text as a point in space. The direction that point sits in represents the meaning of the content. Cosine similarity measures the angle between two of those points. A small angle means the content is very similar in meaning. A large angle means the content is about something quite different.
The score runs from 0 to 1. A score of 1 means the two pieces of content are essentially identical in meaning. A score of 0 means they share no meaningful relationship at all.
Example
Here is what those scores look like with real word pairs:
| Word Pair | Cosine Similarity Score | What It Means |
|---|---|---|
| king vs queen | 0.95 | Nearly identical meaning. Both represent royalty. The model learned they appear in almost the same contexts across millions of documents. |
| dog vs puppy | 0.92 | Very high similarity. Same animal, slightly different context. One is a life stage of the other, so they co-occur constantly in training data. |
| SEO vs search engine optimisation | 0.89 | High similarity. One is the acronym of the other. Models trained on web content learn these are interchangeable and represent the same concept. |
| buy vs purchase | 0.85 | High similarity. Synonyms with nearly identical intent. Important for ecommerce: Google treats these as the same signal on your product pages. |
| backlink vs inbound link | 0.81 | Strong similarity. Two names for the same SEO concept. A page covering both terms signals more complete topical coverage than one using only one term. |
| keyword vs search query | 0.74 | Moderate-high similarity. Related but not identical. A keyword is what you target; a search query is what the user types. Close in meaning but not synonyms. |
| shoes vs sneakers | 0.71 | Moderate similarity. Sneakers are a type of shoe, so they share context but are not interchangeable. For an ecommerce store, both terms matter for category coverage. |
| SEO vs cooking | 0.08 | Near zero. Completely unrelated topics. A link from a cooking blog to your SEO page carries almost no topical relevance signal. |
| cat vs keyboard | 0.04 | Essentially zero. No meaningful semantic connection. These words never appear in the same context in training data, so the model places them in entirely different regions of vector space. |
How SEO & mathematics ties up?
When two words score above 0.7 (for example), LLMs & search engines considers them semantically related. Using both on your page signals broader topical coverage. When two words score below 0.2, there is no meaningful connection. A page or link from that space sends no relevant signal to your rankings.
That is it. The maths behind it involves vectors and dot products, but for SEO purposes the concept is what matters. Cosine similarity tells you how closely related two pieces of text are at a semantic level.
This is different from simple keyword matching. Two sentences can share the same keywords and be about completely different things. Two sentences can share zero keywords and be about exactly the same thing. Cosine similarity captures the second case. Keyword matching does not.
How Google Uses Cosine Similarity in Its Algorithm
Google does not publish its ranking algorithm. But the research it has released tells us a lot about the role cosine similarity plays.
Google’s journey towards semantic search started in 2013 with the Hummingbird update. Before that, Google matched pages to queries primarily by looking for keyword overlap. Hummingbird shifted the focus to understanding the intent behind a query, not just its literal words.
Since then, Google has moved steadily towards representing content as dense vectors, measuring similarity between query vectors and document vectors, and ranking based on that similarity. The exact method has evolved, but the underlying principle is cosine similarity.
BERT and Passage Ranking
The BERT update in 2019 was a major milestone. BERT is a transformer model that reads text bidirectionally, meaning it understands how words relate to each other in context. When BERT processes a query and a document, it produces vector representations of both. Cosine similarity is then one of the tools used to measure how well they match.
Passage ranking, launched in 2021, took this further. Google started ranking individual passages within a page rather than just the page as a whole. That means a single paragraph deep in your article can rank for a query if its vector is close enough to the query’s vector in cosine similarity terms.
MUM and Multimodal Understanding
Multitask Unified Model / MUM went even further. MUM works across text, images, and languages simultaneously. It still uses vector representations and similarity measurements at its core. The scale is simply much larger and the representations far richer than earlier models.
The practical implication is this: Google now measures content quality partly by how semantically complete a page is, not just how many times a keyword appears.
How LLMs Use Cosine Similarity
Large language models like ChatGPT and Claude use cosine similarity constantly. It is fundamental to how they work.
When an LLM is trained, it learns to represent words, phrases, and sentences as vectors in a very high-dimensional space. Words with similar meanings end up in similar regions of that space. The model learns this from patterns across enormous amounts of text.
When the model responds to a query, it uses cosine similarity to find the most relevant information in its training data and to decide which concepts belong together. It is also used in retrieval-augmented generation (RAG) systems, where an LLM retrieves documents based on vector similarity before generating a response.
This matters for SEO because AI-generated answers in search are built on the same vector similarity logic. When Google’s AI Overview or an AI assistant answers a question, it retrieves and weights sources using semantic similarity. Being the most cosine-similar source to a query is increasingly how you get cited.
Actionable tip: Use a tool like Voyage AI or Cohere Embed to generate embeddings for your top-ranking competitors’ pages and your own. Compare cosine similarity scores against the target query. The gap tells you exactly how much semantic ground you need to cover.
Why Cosine Similarity Matters for Your SEO Strategy
Most SEOs still think about optimisation as: find keyword, use keyword on page. That model is not wrong, but it is incomplete.
Google no longer rewards pages that repeat keywords. It rewards pages whose overall semantic content is most relevant to what the searcher is trying to accomplish. Cosine similarity is how it measures that relevance.
If your page has a high cosine similarity to the target query, Google understands that your content is genuinely about that topic. If your cosine similarity is low, adding more keyword mentions will not fix it. The page is simply not semantically rich enough.
Also, as AI Overviews and generative search results become more common, the ranking game is shifting from “which page ranks at position one” to “which source gets cited in the AI answer.” That selection process is heavily influenced by cosine similarity.
For ecommerce stores, this has direct implications. If your product and category pages use thin, templated descriptions, their semantic richness is low. Their cosine similarity to informational and transactional queries is low too. Better-written pages with more contextual depth will consistently outrank them, even if both pages target the same keyword.
Finding Semantic Keywords Using Cosine Similarity
This is where cosine similarity moves from theory to a practical workflow you can run yourself.
Semantic keywords are terms that are conceptually related to your primary keyword, even if they do not share the same words. They signal to Google that your content covers the full topic, not just the surface keyword.
Method 1: Use a Vector Similarity Tool
Tools like the Keyword Insights clustering tool, Surfer SEO, or custom Python scripts can take a seed keyword and find related terms by calculating cosine similarity between word vectors.
The process: enter your primary keyword, run similarity against a database of related terms, and sort by similarity score. Terms with a score above roughly 0.7 are semantically close. Include them naturally throughout your content.
Actionable tip: If you use Python, the sentence-transformers library makes this straightforward. Load a pre-trained model like all-MiniLM-L6-v2, embed your primary keyword and a list of candidate terms, then compute cosine similarity with sklearn.metrics.pairwise.cosine_similarity. Sort descending and take the top 20. You now have a data-driven semantic keyword list. Our guide on using Python for SEO walks through setting up this kind of workflow.
Method 2: Use Existing SERPs as a Proxy
You do not always need to run embeddings yourself. Google has already done the work.
Scrape the top 10 ranking pages for your target keyword. Extract all the headings and body text. Find the terms that appear consistently across multiple top-ranking pages but are not in your own content. Those terms have high cosine similarity to the query, or Google would not reward pages that include them.
Tools like Clearscope, MarketMuse, and Surfer SEO automate this process. They surface the semantically related terms Google’s top results share and tell you which ones your content is missing.
How to Use Cosine Similarity for Content Optimisation
Understanding the concept is one thing. Changing how you write content based on it is another.
The core idea: a high cosine similarity score between your page and the target query means your content is semantically rich and contextually relevant. To achieve this, your content needs to cover the topic thoroughly, not just the keyword surface.
Cover the Full Topic, Not Just the Keyword
Think about what a person searching for your target keyword actually wants to know. Not just the literal answer, but the surrounding context, the common follow-up questions, the related concepts.
A page about “WooCommerce product page SEO” that only covers title tags is semantically thin. A page that covers title tags, meta descriptions, schema markup, image alt text, product descriptions, URL structure, and internal linking has far higher semantic richness. Its cosine similarity to the full range of related queries is much higher.
Use Headers to Signal Semantic Structure
Your H2 and H3 headings are not just for readability. They contribute to the semantic vector of your page. Each heading adds a new layer of meaning that shifts the page’s vector in the direction of more complete topical coverage.
This is why thorough heading structures outperform pages with one or two vague headings. More relevant headings, more complete topical coverage, higher cosine similarity to the full range of related queries.
Actionable tip: Before writing your next product category page, list the top 10 questions a customer has about that product category. Turn each question into an H2 or H3. Then answer it in 2–3 short paragraphs. You will dramatically increase the semantic depth of that page without stuffing a single keyword.
How to Use Cosine Similarity for Backlink Strategy
Most people think of backlinks purely in terms of authority. High DA site links to you, your rankings go up. That is the simplified version.
The more accurate picture is that relevance matters as much as authority. A backlink from a semantically similar page passes more SEO value than a backlink from a high-authority page with no topical connection.
Google measures topical relevance of linking pages using, in part, vector similarity. A link from a page whose content is closely aligned with yours carries more contextual weight. Its content vector is close to yours. The link signals that two semantically related pieces of content are pointing at each other, which makes sense to Google as a genuine editorial signal.
Finding High-Relevance Link Prospects
Use cosine similarity logic to qualify backlink targets before you invest outreach time.
Start with your target page. Identify the core topics and semantic clusters it covers. Then evaluate potential linking pages against those clusters. A page with high topical overlap with your content is a better link prospect than a high-DA page with no topical connection.
Practically, this means targeting blogs, resources, and niche publications in your exact topic space rather than pursuing generic “top domain authority” sites. A link from a mid-authority ecommerce SEO publication to your ecommerce SEO content is worth more than a link from a generic marketing blog with ten times the DA.
Using Anchor Text to Reinforce Semantic Signals
Anchor text is another vector signal. The words in the anchor text contribute to how Google interprets the link. Exact-match anchors are risky at scale, but semantically relevant anchors reinforce the topical connection between the linking page and your page.
When you do outreach, suggest anchor text that is semantically close to your primary keyword without being identical. Instead of “cosine similarity SEO,” suggest “how search engines measure content relevance” or “semantic SEO techniques.” These anchor texts sit close to the same vector space without triggering over-optimisation signals.
The TEI method for ecommerce SEO gives a solid framework for prioritising which pages to build links to first, based on traffic potential and topical authority.
Conclusion: Start Thinking in Meaning, Not Keywords
Cosine similarity is not a new concept. Search engines have used vector similarity for years. What has changed is the scale and sophistication of the models involved.
The shift from keyword SEO to semantic SEO is not a trend. It is where search has been heading for over a decade. Understanding cosine similarity gives you a concrete mental model for why semantic richness matters, how to measure it, and how to improve it across your content and backlink strategy.
Start with your most important pages. Run a semantic keyword gap analysis. Check which related concepts your content is missing. Add topical depth through better headings and supporting content. And when you build links, target pages that are genuinely close to yours in topic space.
These are not quick wins. They are the foundations of rankings that hold.
I am Rasesh Koirala, an ecommerce SEO consultant based in Sydney. If you want to move beyond keyword tactics and build a content and link strategy grounded in how search engines actually work, get in touch. I can help you identify exactly where your semantic gaps are and how to close them.
