Domain-Oriented Knowledge Transfer for Cross-Domain Recommendation

Guoshuai Zhao, Xiaolong Zhang, Hao Tang, Jialie Shen, Xueming Qian

Abstract: Cross-Domain Recommendation (CDR) aims to alleviate the cold-start problem by transferring knowledge from a data-rich domain (source domain) to a data-sparse domain (target domain), where knowledge needs to be transferred through a bridge connecting the two domains. Therefore, constructing a bridge connecting the two domains is fundamental for enabling cross-domain recommendation. However, existing CDR methods often overlook the valuable of natural relationships between items in connecting the two domains. To address this issue, we propose DKTCDR: a Domain-oriented Knowledge Transfer method for Cross-Domain Recommendation. In DKTCDR, We leverages the rich relationships between items in a cross-domain knowledge graph as bridges to facilitate both intra- and inter-domain knowledge transfer. Additionally, we design a cross-domain knowledge transfer strategy to enhance inter-domain knowledge transfer. Furthermore, we integrate the semantic modality information of items with the knowledge graph modality information to enhance item modeling. To support our investigation, we construct two high-quality cross-domain recommendation datasets, each containing a cross-domain knowledge graph. Our experimental results on these datasets validate the effectiveness of our proposed method. Source code is available at https://github.com/zxxxl123/DKTCDR .