Neural Graph Matching based Collaborative Filtering

TL;DR

This paper introduces GMCF, a neural graph matching model that explicitly captures inner and cross attribute interactions for improved recommendation accuracy.

Contribution

The paper proposes a novel neural graph matching framework that distinguishes and models inner and cross attribute interactions in collaborative filtering.

Findings

GMCF outperforms state-of-the-art recommendation models.

Explicit modeling of attribute interactions improves prediction accuracy.

The approach effectively captures both inner and cross attribute interactions.

Abstract

User and item attributes are essential side-information; their interactions (i.e., their co-occurrence in the sample data) can significantly enhance prediction accuracy in various recommender systems. We identify two different types of attribute interactions, inner interactions and cross interactions: inner interactions are those between only user attributes or those between only item attributes; cross interactions are those between user attributes and item attributes. Existing models do not distinguish these two types of attribute interactions, which may not be the most effective way to exploit the information carried by the interactions. To address this drawback, we propose a neural Graph Matching based Collaborative Filtering model (GMCF), which effectively captures the two types of attribute interactions through modeling and aggregating attribute interactions in a graph matching structure for recommendation. In our model, the two essential recommendation procedures, characteristic learning and preference matching, are explicitly conducted through graph learning (based on inner interactions) and node matching (based on cross interactions), respectively. Experimental results show that our model outperforms state-of-the-art models. Further studies verify the effectiveness of GMCF in improving the accuracy of recommendation.