Knowledge-Reinforced Cross-Domain Recommendation

Abstract

Over the past few years, cross-domain recommendation has gained great attention to resolve the cold-start issue. Many existing cross-domain recommendation methods model a preference bridge between the source and target domains to transfer preferences by the overlapping users. However, when there are insufficient cross-domain users available to bridge the two domains, it will negatively impact the recommender system's accuracy (ACC) and performance. Therefore, in this article, we propose to create a link between the source and the target domains by leveraging knowledge graph (KG) as the auxiliary information, and propose a novel knowledge-reinforced cross-domain recommendation (KR-CDR) method. First of all, we construct a new cross-domain KG (CDKG) by using the KGs that represent the source and target domains, respectively. Additionally, we employ reinforcement learning (RL) with meta learning on CDKG to discover meta-paths between the source and target domains. With these meta-paths, we obtain meta-path aggregated embedding vectors for cold-start users. Ultimately, the predicted rating can be acquired from the user meta-path aggregated embedding vector and item embedding vector. Experiments carried out on five real-world datasets show that the proposed method performs better than the state-of-the-art methods.