Graph Attention Collaborative Similarity Embedding for Recommender System

Abstract

We present Graph Attention Collaborative Similarity Embedding (GACSE), a new recommendation framework that exploits collaborative information in the user-item bipartite graph for representation learning. Our framework consists of two parts: the first part is to learn explicit graph collaborative filtering information such as user-item association through embedding propagation with attention mechanism, and the second part is to learn implicit graph collaborative information such as user-user similarities and item-item similarities through auxiliary loss. We design a new loss function that combines BPR loss with adaptive margin and similarity loss for the similarities learning. Extensive experiments on three benchmarks show that our model is consistently better than the latest state-of-the-art models.

References in corpus (6)

• Adam: A Method for Stochastic Optimization
• Semi-Supervised Classification with Graph Convolutional Networks
• BPR: Bayesian Personalized Ranking from Implicit Feedback
• KGAT: Knowledge Graph Attention Network for Recommendation
• Graph Convolutional Matrix Completion
• Collaborative Memory Network for Recommendation Systems