Attribute-aware Collaborative Filtering: Survey and Classification

TL;DR

This paper surveys attribute-aware collaborative filtering models, categorizing them mathematically into four groups, and compares their effectiveness through experiments, providing insights into their design and performance.

Contribution

It offers a comprehensive classification and mathematical interpretation of attribute-aware CF models, along with experimental comparisons of major approaches.

Findings

Mathematically classifies attribute-aware CF models into four categories

Provides mathematical insights for each category

Experimental comparison of major models' effectiveness

Abstract

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