ML Anthology

Towards Open-World Cross-Domain Sequential Recommendation: A Model-Agnostic Contrastive Denoising Approach

Wujiang Xu, Xuying Ning, Wenfang Lin, Mingming Ha, Qiongxu Ma, Qianqiao Liang, Xuewen Tao, Linxun Chen, Bing Han, Minnan Luo
ECML-PKDD 2024 pp. 161-179
doi:10.1007/978-3-031-70341-6_10 /ecmlpkdd/2024/xu2024ecmlpkdd-openworld/

Abstract

Cross-domain sequential recommendation (CDSR) aims to address the data spCH). Recently, some SR approaches have utilized auxiliary behaviors to complement the information for long-tailed users. However, these methods cannot deliver promising performance in CDSR, as they overlook the semantic gap between target and auxiliary behaviors, as well as user interest deviation across domains ( 2nd CH). In this paper, we propose a model-agnostic contrastive denoising (MACD) approach towards open-world CDSR. We introduce auxiliary behavior sequence information (i.e., clicks) into CDSR methods to explore potential interests. Specifically, we design a denoising interest-aware network combined with a contrastive information regularizer to remove inherent noise from auxiliary behaviors and exploit multi-interest from users. Extensive offline experiments on public industry datasets and a standard A/B test on a large-scale financial platform with millions of users both confirm the remarkable performance of our model in open-world CDSR scenarios. Code and dataset are available at https://github.com/WujiangXu/MACD .

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Cite

Text

Xu et al. "Towards Open-World Cross-Domain Sequential Recommendation: A Model-Agnostic Contrastive Denoising Approach." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2024. doi:10.1007/978-3-031-70341-6_10

Markdown

[Xu et al. "Towards Open-World Cross-Domain Sequential Recommendation: A Model-Agnostic Contrastive Denoising Approach." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2024.](https://mlanthology.org/ecmlpkdd/2024/xu2024ecmlpkdd-openworld/) doi:10.1007/978-3-031-70341-6_10

BibTeX

@inproceedings{xu2024ecmlpkdd-openworld,
  title     = {{Towards Open-World Cross-Domain Sequential Recommendation: A Model-Agnostic Contrastive Denoising Approach}},
  author    = {Xu, Wujiang and Ning, Xuying and Lin, Wenfang and Ha, Mingming and Ma, Qiongxu and Liang, Qianqiao and Tao, Xuewen and Chen, Linxun and Han, Bing and Luo, Minnan},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2024},
  pages     = {161-179},
  doi       = {10.1007/978-3-031-70341-6_10},
  url       = {https://mlanthology.org/ecmlpkdd/2024/xu2024ecmlpkdd-openworld/}
}