Intrinsic and Extrinsic Factor Disentanglement for Recommendation in Various Context Scenarios

Abstract

In recommender systems, the patterns of user behaviors (e.g., purchase, click) may vary greatly in different contexts (e.g., time and location). This is because user behavior is jointly determined by two types of factors: intrinsic factors, which reflect consistent user preference, and extrinsic factors, which reflect external incentives that may vary in different contexts. Differentiating between intrinsic and extrinsic factors helps learn user behaviors better. However, existing studies have only considered differentiating them from a single, predefined context (e.g., time or location), ignoring the fact that a user’s extrinsic factors may be influenced by the interplay of various contexts..