GCFA: Graph Neural Networks on Collaborative Filtering Recommendation via Attention Mechanism

Abstract

Ranging from Matrix Factorization to deep learning based methods, recommender systems usually obtain user's (or item's) embedding via mapping from existed features. However, the collaborative signal is not encoded in the embedding process. Graph Neural Networks (GNNs) have been proved to be powerful in learning on graph data. But building recommender systems on GNNs faces challenges: the user-item graph encodes both interactions and opinions; user item interactions have heterogeneous strengths. To address these challenges, we proposed a novel framework on collaborative filtering recommendation via attention mechanism (GCFA), which models heterogeneous user-item interaction strengths and exploits the user-item graph structure by propagating embeddings on it. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed framework GCFA.