Correlation-Aware Next Basket Recommendation Using Graph Attention Networks

Abstract

With the increasing number of commodities in our daily life, the recommender system plays a more and more important role in selecting items of users’ interests. For the next basket recommendation task, in this work, we propose the first end-to-end correlation-aware model to predict the next basket considering intra-basket correlations using graph attention networks. Specifically, items and correlations between items are viewed as nodes and edges in a graph, respectively. By estimating and aggregating the intra-basket correlations using the attention layer of the self-attention model, the recommendation can be conducted at the basket level, instead of at the item level. We conduct comprehensive experiments on a real-world retailing dataset to show the improvement from state-of-the-art baselines using our proposed method.