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# Co-occurrence
**Co-occurrence** measures how frequently two products appear together in the same context — e.g., the same basket, order, or browsing session.\
The more often two products co-exist, the stronger their association score.
#### How it is calculated
A **co-occurrence matrix** `C` is built where each cell `C[i,j]` represents the number of times product `i` and product `j` appear together.
| | iPhone | Case | Charger | Book |
| ----------- | ------ | ---- | ------- | ---- |
| **iPhone** | — | 250 | 180 | 2 |
| **Case** | 250 | — | 40 | 1 |
| **Charger** | 180 | 40 | — | 0 |
The association `iPhone → Case` is much stronger than `iPhone → Book`.
This raw count can be normalized, for example using a **cosine-style similarity score**:
#### Example
* A customer frequently buys an **iPhone** together with a **case** and a **charger** → “Frequently bought together” block.
* A shopper browses **backpacks** and **laptops** in the same session → “Co-viewed products” logic connects them.
#### Key takeaways
* Fully based on real user behavior (purchase or browsing).
* Simple, interpretable, and very effective once there’s enough volume.
* Less reliable for new products with no data yet.
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