Graph Attention Collaborative Similarity Embedding for Recommender System
This paper addresses the problem of improving recommendation accuracy for users by better capturing collaborative information.
This paper introduces Graph Attention Collaborative Similarity Embedding (GACSE), a new recommendation framework that leverages both explicit and implicit collaborative information from user-item bipartite graphs. The model consistently outperforms state-of-the-art models across three benchmark datasets.
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.