Light Graph Convolutional Collaborative Filtering With Multi-Aspect Information

The personalized recommendation has become increasingly prevalent in real-world applications, to help users in discovering items of interest. Graph Convolutional Network (GCN) has achieved great success and become a new state-of-the-art for collaborative filtering. However, most of the existing GCN based methods can only capture information about the user's purchase (or click) history, reflecting only one aspect of the user preferences and item characteristics. To provide more accurate recommendations, we need to go beyond modeling user-item interactions and take auxiliary information into consideration. In this paper, we propose a Light GCN based Aspect-level Collaborative Filtering model (LGC-ACF) to exploit multi-aspect user-item interaction information. First, we construct aspect-level user-item interaction graphs according to the interaction history and the knowledge information of items, then feed them to a delicately designed Light GCN based model to learn aspect-level representations of users and items. Finally, the representations of all aspects and all propagation layers are fused for recommendation. We apply LGC-ACF to three datasets: Movielens, Amazon, and Taobao. The experiment results show that LGC-ACF achieves average NDCG improvements of 5.31%, 4.06%, 14.9% in Movielens, Amazon, and Taobao datasets, respectively, compared with state-of-the-art baselines for recommendation.