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Is TF-IDF a Google ranking factor?

Find out what TF-IDF is, how it works, why it's part of the SEO lexicon, and most importantly – whether Google uses it as a ranking factor.

What is TF-IDF and can it really help your SEO strategy?

You'll be forgiven for thinking, "Those crazy SEO guys...What will they think next?

But this is not a case of this thought leader or trying to coin a new phrase.

In this chapter, you will learn what TF-IDF is, how it works, why it is part of the SEO lexicon, and most importantly – does Google use it as a ranking factor.

TF: term frequency

IDF: inverse document frequency

Disclaimer: TF-IDF is a ranking factor

If you want to learn more about this topic, you've seen some crazy headlines designed to make you feel like you're missing out on not allocating a budget to the TF-IDF this year:

  • TF-IDF for SEO: What works and what doesn't.
  • TF-IDF: The best content optimization tool SEO doesn't use.
  • TF IDF SEO: How to Crush Your Competitors with TF-IDF.

Is TF-IDF the SEO strategy you've been missing?

Evidence for TF-IDF as a ranking factor

Let's start with this: what is TF-IDF?

Term Frequency – Reverse Document Frequencyis a term in the field of information retrieval.

This number expresses the statistical importance of any given word to the entire collection of documents.

In plain language, the more times a word occurs in a document set, the more important it is, and the more weight the term has.

What does this have to do with search?

Well, Google is a huge information retrieval system.

Suppose you have a collection of 500 documents and you want to rank them in an order related to the term [swing].

The first part of the equation, term frequency (TF), will be:

  • ignoreDocuments that do not contain all three words.
  • CalculationThe number of times each term appears in each remaining document.
  • considerFilelength.

What the system finally gets is a TF graph for each document.

But that number alone can be problematic.

Depending on the terminology, you could still end up with a bunch of documents and no real clue as to which is most relevant to your query.

The next step, Inverse Document Frequency (IDF), provides more context for your TF.

Document Frequency = Count terms across the entire collection of documents.

Inverse = Invert the importance of the most frequently occurring terms.

Here, the system removes the term [and] from the equation because we can see that it occurs so frequently across all 500 documents that it is irrelevant for this particular query.

We don't want the document with the most instances [and] to rank the highest.

For documents with the highest weights for [swing] and [roll], normalizing for text length is more likely to be relevant to users looking for information about [swing and scroll].

Evidence against TF-IDF as a ranking factor

The utility of this metric shrinks as the size and variety of document collections grow.

Google's John Mueller touched on this, explaining:

"It's a fairly old indicator and things have changed a lot over the years.There are many other metrics.

I don't think this is not a factor;I think he made it very clear that it's not that important anymore.

As much as one likes to believe that Mueller is trying to pull one on them, he can't be rapping on the issue.

Determining which documents contain the word the searcher is querying is a necessary first step in returning a response.

But having said that, it's an old metric that isn't useful on its own.

In a Google-sized index, the best TF-IDF can do is bring back millions or billions of results.

Can you optimize for it?


Trying to optimize for TF-IDF means trying to achieve a certain keyword density, this is called keyword stuffing.

do not do that.

That doesn't mean the concept is irrelevant to SEO professionals, though.

TF-IDF as a ranking factor: our verdict

Does Google use TF-IDF in its search ranking algorithm, maybe even as a foundational part of its algorithm?

We say absolutely not.

why?Because it is an old (in the age of technology) information retrieval concept.

Today, Google has superior methods for evaluating web pages (eg, word vectors, cosine similarity, and other natural language processing methods).

Knowing if and how often the word a user is searching for appears in a document is just the first step.

TF-IDF is not much for beginners without the myriad other levels of analysis to determine things like expertise, authority, and trust.

This means that TF-IDF is not a tool or strategy that can be used to optimize a website.

You can't do any useful analysis with TF-IDF, nor use it to improve your SEO, because it requires the entire corpus of search results to run the calculations on.

Plus, we've graduated, not just wonderingWhichKeywordfor how to use themand what related topics emerge to ensure the context and intent match our own..

SEO professionals who use the terms TF-IDF and semantic search interchangeably misunderstand TF-IDF.

It just measures how often a word appears in a collection of documents.

Bottom line: Understanding how content is evaluated is important, but that knowledge doesn't always have to lead to another item on your SEO checklist.

Unless you're building your own information retrieval system, TF-IDF is a system where you can chalk it up to fun facts from days gone by and move on.

Extended reading:

Is Tabbed Content a Google Ranking Factor?

Is syndicated content a Google ranking factor?

Is syndicated content a Google ranking factor?

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