Category recommendation with implicit item feedback

Abstract

Techniques of providing category recommendations include a category recommendation system that provides users recommended categories based on implicit data (e.g., user-item interactions) and/or explicit data (e.g., queries, user information). The recommendations can be personalized or non-personalized (i.e., depending if user embeddings are used), queried or non-queried (i.e., depending on whether query embeddings are used), or personalized and queried (if both user and query embeddings are used). In any of these cases, there is an offline mode and a serving mode. In the offline mode, a category embedding is generated from an aggregation of item embeddings associated with a candidate category. In the serving mode, the candidate category is selected for display on a user device based on a similarity between the category embedding and either, or both, of the user embedding and the query embedding.