Is the TF-IDF a Google Ranking Factor?

What exactly is TF-IDF, and how can it benefit your SEO strategy?

“Those crazy SEO people… what will they think of next?” you might wonder.

This isn’t, however, a case of a thought leader trying to coin a new phrase.

This chapter will explain what the TF-IDF is, how it works, why it’s used in SEO, and, most importantly, whether Google uses it as a ranking factor.

TF-IDF Is A Ranking Factor, According To The Claim

If you look up more information on this topic, you’ll come across some bizarre headlines designed to make you feel bad about not allocating budget to TF-IDF this year:

What Works and What Doesn’t Work in SEO, according to the TF-IDF.
The best content optimization tool that SEOs aren’t using is TF-IDF.
TF How to Use TF-IDF to Crush Your Competitors in IDF SEO

Is TF-IDF the SEO strategy that you’ve been looking for?

The TF-IDF As A Ranking Factor: Evidence

Let’s begin with a definition: what is TF-IDF?

A term from the field of information retrieval is the term frequency-inverse document frequency.

It’s a graph that shows the statistical significance of any given word to the entire document collection.

In layman’s terms, the more frequently a word appears in a document collection, the more important it is, and the more heavily that term is weighted.

What exactly does this have to do with search?

Google, on the other hand, is a massive information retrieval system.

Let’s say you have 500 documents and want to rank them in order of their relevance to the term [rocking and rolling].

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

Documents that don’t have all three words should be ignored.
Count how many times each term appears in each document that remains.
Take into account the document’s length.

In the end, the system generates a TF figure for each document.

However, just that figure can be problematic.

Depending on the term, you may still find yourself with a stack of documents and no idea which ones are most relevant to your search.

The next step, inverse document frequency (IDF), adds some context to your TF.

Counting terms across the document collection is known as document frequency.

Inverting the importance of the most frequently appearing terms is known as inversion.

The term [and] is removed from the equation because it appears so frequently across all 500 documents that it is irrelevant to this particular query.

We don’t want the documents with the most [and] to be ranked first.

When normalizing for text length, documents with the highest weighting for [rocking] and [rolling] are more likely to be relevant to people looking for information on [rocking and rolling].

TF-IDF As A Ranking Factor: The Evidence

The utility of this metric decreases as the document collection grows in size and variety.

Google’s John Mueller addressed the issue and stated that

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

I don’t think he’s saying it’s not a factor; I think he’s just saying it’s not as important as it once was.

And, as much as some people want to believe Mueller is trying to deceive them, he’s not lying on this one.

A necessary first step in returning a response is determining which documents contain the words a searcher is looking for.

However, it’s an old metric that isn’t particularly useful on its own.

In an index the size of Google’s, TF-IDF could only return millions or billions of results at best.

Are you able to optimize for it?

No.

Keyword stuffing is when you try to optimize for TF-IDF by trying to achieve a certain keyword density.

That is not something you should do.

That isn’t to say that SEO professionals don’t care about this concept.

Our Opinion On The TF-IDF As A Ranking Factor

Is TF-IDF used by Google in its search ranking algorithm – possibly as a foundational part of it?

No, we don’t believe so.

Why? Because it’s an old (in technological terms) concept for retrieving information.

Google now has far superior methods for evaluating web pages (e.g., word vectors, cosine similarity, and other natural language processing methods).

Knowing whether and how often a user’s search term appears in a document is only the first step.

Without a plethora of other layers of analysis to determine things like expertise, authoritativeness, and trust, the TF-IDF doesn’t account for much.

That means TF-IDF isn’t a tool or strategy you can employ to improve your site’s performance.

Because TF-IDF requires the entire corpus of search results to run the calculation against, you can’t do any useful analysis with it or use it to improve your SEO.

Furthermore, we’ve progressed from simply wanting to know what keywords are used to also wanting to know how they’re used and what related topics come up, in order to ensure that the context and intent match our own.

SEO professionals who mix up the terms TF-IDF and semantic search are misusing TF-IDF.

It’s simply a count of how many times a word appears in a set of documents.

Bottom line: It’s critical to understand how content is evaluated, but this knowledge doesn’t always have to translate into an additional item on your SEO to-do list.

Unless you’re building your own information retrieval system, TF-IDF is something you can chalk up as a fun fact from the past and move on.