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.

Highlights

  • Collaborative filtering is arguably the most effective method for building a recommender system

  • Model We consider several popular models that are representative of each category for comparison: the Regression-based Latent Factor Model (RLFM) (Agarwal and Chen, 2009) and FriendshipInterest Propagation (FIP) (Yang et al, 2011) in Discriminative Matrix Factorization, Collective Matrix Factorization (CMF) (Singh and Gordon, 2008) in Generative Matrix Factorization, and Tensor Factorization (TF) (Karatzoglou et al, 2010), the Factorization Machine (FM) (Rendle et al, 2011), and the Neural Factorization Machine (NFM) (He and Chua, 2017) in Generalized Factorization

  • FM can be seen as a design that incorporates attributes into matrix factorization (MF), and the same applies for Neural Collaborative Filtering (NCF)+ vs. NCF

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Summary

INTRODUCTION

Collaborative filtering is arguably the most effective method for building a recommender system. Researchers have proposed different methods to extend existing collaborative filtering models in recent years, such as factorization machines, probabilistic graphical models, kernel tricks, and models based on deep neural networks. We notice that those papers can be categorized based on the type of attributes incorporated into the models. Discriminative matrix factorization models extend the traditional MF by treating the attributes as prior knowledge to learn the latent representation of users or items. Generalized factorization models view the user/item identity as a kind of attribute, and various models have been designed for determining the lowdimensional representation vectors for rating prediction.

Problem Definition of Recommender Systems
Collaborative Filtering and Matrix Factorization
Overview
Sources of Attributes
Side Information
Contexts
Converting Side Information to Contexts
Attribute Types
Categorical Attributes
Rating Types
Explicit Opinions
Implicit Feedback
Recommendation Goals
Rating Prediction
COMMON MODEL DESIGNS OF ATTRIBUTE-AWARE RECOMMENDER SYSTEMS
Discriminative Matrix Factorization
Attributes in a Bilinear Model
Generative Matrix Factorization
Attributes in Deep Neural Networks
Generalized Factorization
Modeling User-Item-Rating Interactions Using Heterogeneous Graphs
Differences Between Models
EMPIRICAL COMPARISON
Experimental Setup
Performance Comparisons
Findings and Discussion
CONCLUSION
Full Text
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