A Revisit to The Identification of Contexts in Recommender Systems

Abstract

In contrast to traditional recommender systems (RS), context-aware recommender systems (CARS) emerged to adapt to users' preferences in various contextual situations. During those years, different context-aware recommendation algorithms have been developed and they are able to demonstrate the effectiveness of CARS. However, this field has yet to agree on the definition of context, where researchers may incorporate diversified variables (e.g., user profiles or item features), which further creates confusions between content-based RS and context-based RS, and positions the problem of context identification in CARS. In this paper, we revisit the definition of contexts in recommender systems, and propose a context identification framework to clarify the preliminary selection of contextual variables, which may further assist interpretation of contextual effects in RS.