Introducing knowledge graphs as side information into recommendation can effectively mitigate data sparsity and cold start problems. However, the existing methods cannot simultaneously acquire the feature information and structure information of the knowledge graph. In this paper, we propose RKG, a multi-task semantic feature and high-order structure learning approach for knowledge graph assisting recommendation. RKG, which consists of a Recommender module, a Knowledge graph feature learning module and a knowledge Graph structure learning module, is a deep end-to-end framework that utilizes knowledge graph’s latent information to enhance the recommender’s performance. Information is shared among the three modules through cross unit and exchange unit to interact automatically with multiple learning tasks. Extensive experiments on three public datasets show that RKG has achieved superior and stable performance in top-K recommendation, click-through rate prediction and sparse user-item interaction scenarios over several state-of-the-art baselines.
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