Multi-Behavior Recommendation with Hyperbolic Geometry

Even though users interacted diversely on items (e.g., click, add-to-cart, and buy), traditional recommendations were mostly built using only the user-item interaction data on the target behavior (e.g., buy), making them suffer from the severe data sparsity issue. To alleviate the problem, recent works on multi-behavior recommendation incorporated multiple types of user-item interactions such as click, add-to-cart, and buy. However, the latest approaches are still limited by overlooking early-stage interactions, and have limited expressiveness of Euclidean geometry. To overcome these issues, in this paper, we propose a Multi-behavior Hyperbolic Graph Recommender (MB-HGR) with two novel aspects. First, it uses multiple heterogeneous graphs to learn multiple user behavior types, where each heterogeneous graph represents a user-item interaction type. This will help not only alleviate the serious data sparsity problem, but also allow the model to explicitly weight different behavior types and prevent information loss. Second, it leverages the expressiveness of the hyperbolic geometry over Euclidean geometry, where exponential growth of distances in the hyperbolic geometry matches the exponential growth of nodes in the hierarchical structures and learns better users/items representations. Experimental results on two public benchmark datasets show that on average our proposed model achieves a significant improvement of 28.32% at Recall@10 and 30.14% at NDCG@10 over the best baseline.