> 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/semantic-similarity.md).

# Semantic Similarity

**Semantic similarity** measures how closely products are related in *meaning* — based on their text descriptions, attributes, and metadata — rather than behavioral signals.

#### How it is calculated

Each product description and metadata is encoded into a **dense vector (embedding)** using multilingual transformer models (e.g., SBERT, FastText).\
The algorithm computes the **cosine similarity** between product vectors:

<figure><img src="/files/yqgKVN5yQrNSJ9vPQ5ii" alt=""><figcaption></figcaption></figure>

Where:

* `v_i`, `v_j` = product embedding vectors
* Similarity ∈ \[0,1], with 1 meaning semantically identical.

#### Example

* Source product: *“Nike Air Zoom Pegasus 40 running shoe, red”*\
  → Semantically similar products might include:
  * *“Adidas Ultraboost 22 running shoe, blue”* (same purpose and category)
  * *“Salomon Speedcross trail shoe”* (related usage, similar function)
  * ❌ *“Red dress”* (similar color but irrelevant meaning — model filters this out).

#### Multilingual model

The embedding model is trained on **109 languages**, including those without spaces (Japanese, Chinese, Thai, etc.), allowing semantic matching across all markets.\
This differs from the *Content Interest Criterion* used in the Segment Builder, which only supports Western languages.

#### Key takeaways

* Works from day one — no behavioral data needed.
* Ideal for new, long-tail, or low-traffic products.
* Enables “Similar products” and “Alternative discovery” strategies.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.abtasty.com/recommendations-and-merchandising/concepts/strategies/rules/presets-default-algorithms/algorithms-usage/semantic-similarity.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.