TY - GEN
T1 - Towards Open-World Cross-Domain Sequential Recommendation
T2 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2024
AU - Xu, Wujiang
AU - Ning, Xuying
AU - Lin, Wenfang
AU - Ha, Mingming
AU - Ma, Qiongxu
AU - Liang, Qianqiao
AU - Tao, Xuewen
AU - Chen, Linxun
AU - Han, Bing
AU - Luo, Minnan
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Contrastive Learning
KW - Cross-Domain Sequential Recommendation
KW - Open-world Recommendation
KW - Sequential Recommendation
UR - https://www.scopus.com/pages/publications/85203605313
U2 - 10.1007/978-3-031-70341-6_10
DO - 10.1007/978-3-031-70341-6_10
M3 - 会议稿件
AN - SCOPUS:85203605313
SN - 9783031703409
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 161
EP - 179
BT - Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2024, Proceedings
A2 - Bifet, Albert
A2 - Davis, Jesse
A2 - Krilavičius, Tomas
A2 - Kull, Meelis
A2 - Ntoutsi, Eirini
A2 - Žliobaitė, Indrė
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 9 September 2024 through 13 September 2024
ER -