Multi-domain sequential recommendation via multi-sequence and multi-task learning
Liwei Pan, Weike Pan, Zhong Ming · Information Processing & Management · 2025
In recent years, more and more researchers and practitioners have focused on multi-domain CTR prediction and achieved great success. Though users’ behaviors often exhibit sequentiality, little effort has been made on multi-domain sequential recommendation (MDSR). Most existing works on MDSR sort the interactions from all domains in chronological order and then predict the next interacted items in each domain. However, they neglect separate interaction sequences in each domain. Therefore, they cannot exploit the commonalities and differences among different domains well. Cross-domain sequential recommendation (CDSR) models are usually designed for performance improvement in one target domain rather than in each domain. Although extending a CDSR model to an MDSR one directly or indirectly is feasible, it will result in high time complexity. Meanwhile, they often ignore data imbalance across different domains, which might cause negative transfer. As a response, we propose a novel MDSR solution called multi-sequence multi-task learning (MML). Our MML consists of three modules, including hybrid-domain sequential preference learning (HSPL), intra-domain sequential preference learning (ISPL) and multi-task learning & prediction (MLP). Specifically, HSPL aims to learn hybrid-domain sequential preferences. Meanwhile, we construct augmented sequences and leverage contrastive learning to learn more unbiased hybrid-domain sequential preferences for alleviating negative transfer. ISPL is designed to capture intra-domain sequential preferences. In the MLP module, three specific tasks and a behavior regularizer are leveraged to ensure that each module can learn the corresponding preferences sufficiently and enhance knowledge transfer among different domains. We conduct extensive experiments on some public datasets using different backbone models and show that our MML is able to achieve significantly better performance than the state-of-the-art methods in two or more domains in most cases. Meanwhile, our MML can achieve the same time complexity as the MDSR models only using hybrid interaction sequences. The source code can be found at https://github.com/plw2019/MML .