Recommendation system using aggregated behavioral associations that maps items to related items for personalized shopping suggestions.
It analyzes users’ purchase and behavioral histories in aggregate to detect item-to-item associations, then stores them in an item-to-related-items mapping used for recommendations. The technical significance is that it turns patterns across many users into direct linkage scores, enabling fast “customers also viewed/bought” style retrieval without per-user model training. Previously, deploying this specific association-mapping approach for catalog recommendations could require licensing under the patent covering the recommendation mechanism.
Launch e-commerce cross-sell and “related products” features powered by item-association mapping for marketplaces or subscription boxes.
A computer-implemented service analyzes purchase histories and/or other types of behavioral data of users on an aggregated basis to detect and quantify associations between particular items represented in an electronic catalog. The detected associations are stored in a mapping structure that maps items to related items, and is used to recommend items to users of the electronic catalog. The items may include products and/or categories of products.
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