Jointing Knowledge Graph and Neural Network for Top-N Recommendation

Abstract

Currently, neutral networks attract much attention and show great potential in recommendation systems. The existing works mainly aim at leveraging neural network to model the nonlinear representations of users and items. However, they only use historical interaction sequence of user-items to learn the latent features of users and items, while ignoring the rich self-attributes of items. Recent methods utilize knowledge graphs as auxiliary information to learn the latent features between users and items, but they fail to represent the relevance and similarity of attributes among items. Based on this observation, we propose a novel model named JKN that incorporates knowledge graph and a neural network for item recommendation. The key point of JKN is to learn accurate latent representations of item attributes through knowledge graph, then to integrate them into a feedforward neural network to model user-item interactions in nonlinear. Empirical results on a real-world dataset demonstrate the superior performance of our model in Top-n recommendation task.