> For the complete documentation index, see [llms.txt](https://docs.abtasty.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.abtasty.com/recommendations-and-merchandising/concepts/strategies/rules/presets-default-algorithms/algorithms-usage/tf-idf-term-frequency-inverse-document-frequency.md).

# TF-IDF (Term Frequency – Inverse Document Frequency)

**TF-IDF** is a weighting method that adjusts the co-occurrence score to avoid the dominance of overly generic products (e.g., batteries, gift cards).\
It’s borrowed from information retrieval (used in search engines).

#### How it is calculated

For each pair of products `i` and `j`, the frequency of co-occurrence (`TF`) is weighted by an *inverse frequency* term (`IDF`):

<figure><img src="/files/0vDNdhaSF9wtr1Gi7jpc" alt=""><figcaption></figcaption></figure>

Where:

* **TF(i,j)** = co-occurrence frequency between products `i` and `j`
* **N** = total number of products
* **n\_j** = number of distinct products co-occurring with product `j`

This down-weights products that appear in too many contexts, giving more weight to specific, relevant relationships.

#### Example

* Without TF-IDF: “Book” → “Gift card” (appears in every basket).
* With TF-IDF: “Book” → “Next volume in the same series” (more relevant association).

#### Key takeaways

* Increases recommendation relevance and diversity.
* Surfaces niche or underexposed products.
* Reduces noise from universally popular items.


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