An efficient recommender system based on improved KGPolicy module

As an appropriate method for mining negative signals from large-scale unobserved data, negative sampling plays a key role in recommender systems. Existing negative sampling methods, however, either static or adaptive ones, are insufficient to explicitly learn the structural representation information of user-item interactions. In this study, we hypothesize that indirect interactions between users and items, which enrich the structural representation information, could be useful to explicitly yield informative and high-quality negative samples. To fully exploit the indirect user-item interactions to generate negative samples, we construct a recommender system based on improved knowledge graph policy network (IKGPolicy), which works as a valid module to explore high-quality negatives. Specifically, by improving the graph learning module of the KGPolicy model, it is explicit to capture indirect and high-order interactive information, and yield a informative negative item to train the recommender. Experiments on the Amazon-Book, Last-FM, and Yelp2018 datasets show that our proposed method presents significant improvements over both negative sampling methods and knowledge graph-based models. Further analyses provide sufficient insights in capturing and learning the structural and interactive representation information.