An embedding represents an object as a numerical vector, placing objects with similar meaning or structure near one another in representation space. In retail, product embeddings capture patterns in purchasing behavior rather than only product metadata. section 6.7.1
A practical product-embedding pipeline follows these steps:
- Aggregate customer–product transactions into a user-item matrix. section 6.7.1
- Factorize the matrix to calculate product embeddings. section 6.7.1
- Filter missing or duplicated transaction values before aggregation. section 6.7.1
Retail purchase patterns
A separate pipeline uses customer ID, product ID, and transaction history to create vectors whose structure reflects how products are purchased together or by similar customers. section 6.7.1
