{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/rethinking-cross-domain-sequential","title":"Rethinking Cross-Domain Sequential Recommendation under Open-World Assumptions","arxiv_id":"2311.04590","date":"2023-11-08","proceeding":null,"authors":["Wujiang Xu","Qitian Wu","Runzhong Wang","Mingming Ha","Qiongxu Ma","Linxun Chen","Bing Han","Junchi Yan"],"abstract":"Cross-Domain Sequential Recommendation (CDSR) methods aim to tackle the data sparsity and cold-start problems present in Single-Domain Sequential Recommendation (SDSR). Existing CDSR works design their elaborate structures relying on overlapping users to propagate the cross-domain information. However, current CDSR methods make closed-world assumptions, assuming fully overlapping users across multiple domains and that the data distribution remains unchanged from the training environment to the test environment. As a result, these methods typically result in lower performance on online real-world platforms due to the data distribution shifts. To address these challenges under open-world assumptions, we design an \\textbf{A}daptive \\textbf{M}ulti-\\textbf{I}nterest \\textbf{D}ebiasing framework for cross-domain sequential recommendation (\\textbf{AMID}), which consists of a multi-interest information module (\\textbf{MIM}) and a doubly robust estimator (\\textbf{DRE}). Our framework is adaptive for open-world environments and can improve the model of most off-the-shelf single-domain sequential backbone models for CDSR. 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