Rethinking Cross-Domain Sequential Recommendation under Open-World Assumptions
Name
3589334.3645351.pdf
Size
1.41 MB
Format
Adobe PDF
Checksum (MD5)
266888cb4eea6c374b428ffe302fcf2a
Author(s)
Xu, Wujiang
Wu, Qitian
Wang, Runzhong
Ha, Mingming
Ma, Qiongxu
Chen, Linxun
Han, Bing
Yan, Junchi
Date Issued
May 13, 2024
Publisher
ACM
Citation
Xu, Wujiang, Wu, Qitian, Wang, Runzhong, Ha, Mingming, Ma, Qiongxu et al. 2024. "Rethinking Cross-Domain Sequential Recommendation under Open-World Assumptions."
Version
Final published version
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 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. To promote related research in the community under open-world assumptions, we collected an industry financial CDSR dataset from Alipay, called "MYbank-CDR". Extensive offline experiments on four industry CDSR scenarios including the Amazon and MYbank-CDR datasets demonstrate the remarkable performance of our proposed approach. Additionally, we conducted a standard A/B test on Alipay, a large-scale financial platform with over one billion users, to validate the effectiveness of our model under open-world assumptions. Code and dataset are available at https://github.com/WujiangXu/AMID.
Description
WWW '24: Proceedings of the ACM on Web Conference May 13–17, 2024, Singapore, Singapore
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3589334.3645351
