> 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:
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.
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