Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification

Abstract

Attribute-aware CF models aim at rating prediction given not only the historical rating given by users to items but also the information associated with users (e.g., age), items (e.g., price), and ratings (e.g., rating time). This paper surveys work in the past decade to develop attribute-aware CF systems and finds that they can be classified into four different categories mathematically. We provide readers not only with a high-level mathematical interpretation of the existing work in this area but also with mathematical insight into each category of models. Finally, we provide in-depth experiment results comparing the effectiveness of the major models in each category.

Keywords: attribute; collaborative filtering; matrix factorization; recommender system; survey.