Gated Hypergraph Neural Network for Scene-Aware Recommendation

To improve e-commercial recommender systems, researchers have never stopped exploring the interactions between users and items. Unfortunately, most existing methods only explore one or some certain components of the entire interactions. In fact, the entire interaction process is much richer and more complex, including but not limited to "who purchases what items in which merchant under what interaction environments" . Furthermore, many interactions have common features, thus forming a scene, a kind of prior knowledge for predicting user interactions. In this paper, we make the first attempt to study the scene-aware recommendation, which provides better recommendations with the entire interaction modeling and the scene prior knowledge. To this end, we propose a novel gated hypergraph neural network for Scene-aware Recommendation (SREC). Particularly, we first construct a heterogeneous scene hypergraph to model the entire interactions and scene prior knowledge. Then we propose a novel scene-aware gate mechanism-based hypergraph neural network to enrich their representations. Finally, we design a separable score function to predict the matching scores among user, scene, merchant and interaction environments for training and inference procedures. Extensive experiments demonstrate that our SREC can fully leverage the scene prior knowledge and outperforms state-of-the-art methods on real industrial datasets.