MIBR: Bridging Domains through Diverse Interests for Cross-Domain Sequential Recommendation

Chengzhe Zhang, Xu Min, Changsheng Li, Xiaolu Zhang, Weichang Wu, Jun Zhou, Ye Yuan, Guoren Wang

Abstract: Cross-Domain Sequential Recommendation (CDSR) aims to enhance personalized user experiences by leveraging user behaviors across multiple domains. Existing methods primarily focus on fusing information from various domains and modeling global user preferences, but often struggle with negative transfer, where knowledge from one domain impairs recommendation performance in another. For example, a user may enjoy watching sports games in the video domain but have no interest in participating in sports activities. Consequently, this interest does not extend to purchasing related sports gear. In such cases, a recommendation system suggesting sports gear based on the user’s viewing preferences may not elicit a positive response. To tackle this issue, we propose a novel method called Multi-Interest Bridge Recommender (MIBR). In light of the cross-domain scenario, where user preferences are not entirely consistent across domains, we design a Multi-Interest Extraction (MIE) module to capture the diversity of user interests based on a soft clustering approach. In the meantime, we design a cross-domain bridging (CDB) module, with the goal of mitigating the issue of negative transfer. CDB leverages the extracted interests as a bridge for inter-domain information transfer, enabling each domain to adaptively extract relevant information from diverse interests while ignoring unrelated ones. Extensive experiments on three popular datasets reveal MIBR’s significant superiority over baselines, e.g., with up to a 59.27% uplift in terms of HR@10 over C2DSR on the Movie-Book dataset.