Knowledge-aware Coupled Graph Neural Network for Social Recommendation

TL;DR

This paper introduces KCGN, a graph neural network model that incorporates inter-dependent knowledge across items and users, capturing dynamic multi-typed interactions to improve social recommendation accuracy.

Contribution

The work proposes a novel knowledge-aware coupled GNN that jointly models item and user knowledge, addressing interaction heterogeneity and dynamics in social recommendation.

Findings

KCGN outperforms strong baselines on real-world datasets.

Effectively captures high-order user-item relations.

Handles dynamic multi-typed interactions.

Abstract

Social recommendation task aims to predict users' preferences over items with the incorporation of social connections among users, so as to alleviate the sparse issue of collaborative filtering. While many recent efforts show the effectiveness of neural network-based social recommender systems, several important challenges have not been well addressed yet: (i) The majority of models only consider users' social connections, while ignoring the inter-dependent knowledge across items; (ii) Most of existing solutions are designed for singular type of user-item interactions, making them infeasible to capture the interaction heterogeneity; (iii) The dynamic nature of user-item interactions has been less explored in many social-aware recommendation techniques. To tackle the above challenges, this work proposes a Knowledge-aware Coupled Graph Neural Network (KCGN) that jointly injects the inter-dependent knowledge across items and users into the recommendation framework. KCGN enables the high-order user- and item-wise relation encoding by exploiting the mutual information for global graph structure awareness. Additionally, we further augment KCGN with the capability of capturing dynamic multi-typed user-item interactive patterns. Experimental studies on real-world datasets show the effectiveness of our method against many strong baselines in a variety of settings. Source codes are available at: https://github.com/xhcdream/KCGN.