Rethinking Cross-Domain Sequential Recommendation under Open-World Assumptions
CoRR(2023)
摘要
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 Adaptive
Multi-Interest Debiasing framework for cross-domain
sequential recommendation (AMID), which consists of a multi-interest
information module (MIM) and a doubly robust estimator (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. Our
MIM establishes interest groups that consider both overlapping and
non-overlapping users, allowing us to effectively explore user intent and
explicit interest. To alleviate biases across multiple domains, we developed
the DRE for the CDSR methods. We also provide a theoretical analysis that
demonstrates the superiority of our proposed estimator in terms of bias and
tail bound, compared to the IPS estimator used in previous work.
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关键词
sequential,cross-domain,open-world
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