Multi-Domain Recommendation to Attract Users via Domain Preference Modeling
<|reference_start|>Multi-Domain Recommendation to Attract Users via Domain Preference Modeling: Recently, web platforms have been operating various service domains simultaneously. Targeting a platform that operates multiple service domains, we introduce a new task, Multi-Domain Recommendation to Attract Users (MDRAU), which recommends items from multiple ``unseen'' domains with which each user has not interacted yet, by using knowledge from the user's ``seen'' domains. In this paper, we point out two challenges of MDRAU task. First, there are numerous possible combinations of mappings from seen to unseen domains because users have usually interacted with a different subset of service domains. Second, a user might have different preferences for each of the target unseen domains, which requires that recommendations reflect the user's preferences on domains as well as items. To tackle these challenges, we propose DRIP framework that models users' preferences at two levels (i.e., domain and item) and learns various seen-unseen domain mappings in a unified way with masked domain modeling. Our extensive experiments demonstrate the effectiveness of DRIP in MDRAU task and its ability to capture users' domain-level preferences.<|reference_end|>
@article{ju2024multi-domain,
    title={Multi-Domain Recommendation to Attract Users via Domain Preference
  Modeling},
    author={Hyunjun Ju, SeongKu Kang, Dongha Lee, Junyoung Hwang, Sanghwan Jang,
  Hwanjo Yu},
    journal={arXiv preprint arXiv:2403.17374},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.17374},
    primaryClass={cs.IR}
}