TF-IDF: Is It A Google Ranking Factor?

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

You’d be forgiven for thinking, “These crazy SEO people… what are they going to think of next?”

But this is not the case of this thought leader or attempting 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 – whether Google uses it as a ranking factor.

The claim: TF-IDF is a ranking factor

If you’re looking to learn more about it, you’ll see some wild titles designed to make you feel like you’ve missed out by not budgeting for TF-IDF this year:

  • TF-IDF for SEO: What works and what doesn’t.
  • TF-IDF: The best content optimization tool you don’t use.
  • TF IDF SEO: How to crush your competition with TF-IDF.

Is TF-IDF the SEO Tactic You’ve Been Missing?

Evidence for TF-IDF as a classification agent

Let’s start with this: What is TF-IDF?

Term frequency – the inverse document frequency It is a term from the field of information retrieval.

It is a number that expresses the statistical significance of any given word for the set of documents as a whole.

In simple language, the more a word appears in a set of documents, the more important it is, and the more weight this term has.

What does that have to do with research?

Well, Google is one of the giant information retrieval systems.

Let’s say you have a set of 500 documents and you want to arrange them as they relate to a term [rocking and rolling].

The first part of the equation, the frequency term (TF), would go to:

  • Ignore documents that does not contain all three words.
  • Count the The number of times each term appears in every remaining document.
  • factor in height from the document.

What the system ends up with is the TF number of each document.

But that number alone can be problematic.

Depending on the term, you could still end up with a pile of documents and no real clues as to which one is more relevant to your query.

The next step, Inverse Document Frequency (IDF), gives your TF a little more context.

Document Frequency = Computation of terms across the document set.

Inverse = the inverse of the importance of the most frequently seen terms.

Here, the system removes the term [and] from the equation because we can see that it occurs so recursively across all 500 documents that it is not relevant to this particular query.

We don’t want documents that contain the most cases [and] ranked higher.

The highest weight of documents [rocking] And [rolling] While normalizing text length is likely to be relevant to people looking for information on [rocking and rolling].

Evidence against TF-IDF as a ranking factor

As the collection of documents increases in size and variety, the usefulness of this metric decreases.

John Mueller of Google talked about this and explained it

“This is a fairly old metric and things have evolved quite a bit over the years. There are a lot of other metrics as well.”

I don’t think that says it’s not a factor; I think he’s clearly saying it doesn’t matter anymore.

And as much as people like to believe that Mueller is trying to pull one out of them, there’s no way he’s beating this up.

Identifying which documents contain the words the researcher is querying is a necessary first step in returning a response.

But still, it is an old metric that is not useful in and of itself.

On an index the size of Google, the best TF-IDF can do is return millions or billions of results.

Can you improve it?


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

Do not do it.

However, this does not mean that this concept is not of interest to SEO professionals.

TF-IDF as a ranking factor: our judgment

Does Google use TF-IDF in its search ranking algorithm – perhaps as a core part of its algorithm?

We definitely say no.

why? Because it is an old (in the technological years) concept of retrieving information.

Today, Google has superior methods for evaluating web pages (for example, 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 isn’t much without the myriad other layers of analysis to define things, like experience, authority, and trust, for starters.

This means that TF-IDF is not a tool or tactic you can use to improve your site.

You can’t do any useful kind of analysis with TF-IDF, or use it for SEO optimization, because it takes a whole bunch of search results to run the computation against.

In addition, we have moved beyond the mere desire to know what Keywords are used for How do They are used and the relevant topics that are raised, to make sure the context and purpose match ours.

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

It is simply a measure of the number of times a word appears in a set of documents.

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

Unless you are building your own information retrieval system, TF-IDF is one that you can consider an interesting fact about days gone by moving forward.

Featured image: Robin Biong / Search Engine Journal

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