Path-guided intelligent switching over knowledge graphs with deep reinforcement learning for recommendation

Online recommendation systems process large amounts of information to make personalized recommendations. There has been some progress in research on incorporating knowledge graphs in reinforcement learning for recommendation; however, some challenges still remain. First, in these approaches, an agent cannot switch paths intelligently, because of which, the agent cannot cope with multi-entities and multi-relations in knowledge graphs. Second, these methods do not have predefined targets and thus cannot discover items that are closely related to user-interacted items and latent rich semantic relationships. Third, contemporary methods do not consider long rational paths in knowledge graphs. To address these problems, we propose a deep knowledge reinforcement learning (DKRL) framework, in which path-guided intelligent switching was implemented over knowledge graphs incorporating reinforcement learning; this model integrates predefined target and long logic paths over knowledge graphs for recommendation systems. Specifically, the designed novel path-based intelligent switching algorithm with predefined target enables an agent to switch paths intelligently among multi-entities and multi-relations over knowledge graphs. In addition, the weight of each path is calculated, and the agent switches paths between multiple entities according to path weights. Furthermore, the long logic path has better recommendation performance and interpretability. Extensive experiments with actual data demonstrate that our work improves upon existing methods.The experimental results indicated that DKRL improved the baselines of NDCG@10 by 3.7%, 9.3%, and 4.7%; of HR@10 by 12.39%, 20.8%, and 13.86%; of Prec@10 by 5.17%, 3.57%, 6.2%; of Recall@10 by 3.01%, 4.2%, and 3.37%. The DKRL model achieved more effective recommendation performance using several large benchmark data sets compared with other advanced methods.