Neural Graph Filtering for Context-aware Recommendation

With the rapid development of web services, various kinds of context data become available in recommender systems to handler the data sparsity problem, called context-aware recommendation (CAR). It is challenging to develop effective approaches to model and exploit these various and heterogeneous data. Recently, heterogeneous information network (HIN) has been adopted to model the context data due to its flexibility in modelling data heterogeneity. However, most of the HIN-based methods, which rely on meta paths or graph embedding to extract features from HINs, cannot fully mine the network structure and semantic features of users and items. Besides, these methods, utilizing the global dataset to learn personalized latent factors, usually suffer individuality loss problem. In this paper, we propose a neural graph filtering method for context-aware recommendation, called NGF. First, we use an unified HIN to model both the users’ feedback information and the context data. Then, we adopt graph filtering to predict aspect-level ratings on a series of independent subgraphs of the unified HIN and feed a deep neural network (DNN) to fuse the predictions for CAR. Concretely, graph filtering is a case-by-case algorithm for personalized recommendation on HINs, which predicts the further behavior by all its similar historical behaviors. We split the unified HIN into many single-aspect networks according to the semantic relations and utilize graph filtering to predict user’s behavior on each subgraphs. The following deep neural network is to fuse the personalized predictions in aspect-level. Extensive experiments on two real-world datasets demonstrate the effectiveness of our neural graph filtering for CAR.