Improved Diversity-Promoting Collaborative Metric Learning for Recommendation

doi:10.1109/TPAMI.2024.3412687

	Improved Diversity-Promoting Collaborative Metric Learning for Recommendation
	Bao, Shilong 1,2; Xu, Qianqian 3; Yang, Zhiyong 4; He, Yuan 5; Cao, Xiaochun 6; Huang, Qingming 3,7,8
	2024-12-01
发表期刊	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
ISSN	0162-8828
卷号	46 期号:12 页码:9004-9022
摘要	Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and collaborative filtering. Following the convention of RS, existing practices exploit unique user representation in their model design. This paper focuses on a challenging scenario where a user has multiple categories of interests. Under this setting, the unique user representation might induce preference bias, especially when the item category distribution is imbalanced. To address this issue, we propose a novel method called Diversity-Promoting Collaborative Metric Learning (DPCML), with the hope of considering the commonly ignored minority interest of the user. The key idea behind DPCML is to introduce a set of multiple representations for each user in the system where users' preference toward an item is aggregated by taking the minimum item-user distance among their embedding set. Specifically, we instantiate two effective assignment strategies to explore a proper quantity of vectors for each user. Meanwhile, a Diversity Control Regularization Scheme (DCRS) is developed to accommodate the multi-vector representation strategy better. Theoretically, we show that DPCML could induce a smaller generalization error than traditional CML. Furthermore, we notice that CML-based approaches usually require negative sampling to reduce the heavy computational burden caused by the pairwise objective therein. In this paper, we reveal the fundamental limitation of the widely adopted hard-aware sampling from the One-Way Partial AUC (OPAUC) perspective and then develop an effective sampling alternative for the CML-based paradigm. Finally, comprehensive experiments over a range of benchmark datasets speak to the efficacy of DPCML.
关键词	Collaborative metric learning (CML) machine learning partial AUC optimization recommendation system
DOI	10.1109/TPAMI.2024.3412687
收录类别	SCI
语种	英语
资助项目	National Key R&D Program of China[2018AAA0102000] ; National Natural Science Foundation of China[62236008] ; National Natural Science Foundation of China[U21B2038] ; National Natural Science Foundation of China[U23B2051] ; National Natural Science Foundation of China[61931008] ; National Natural Science Foundation of China[62122075] ; National Natural Science Foundation of China[61976202] ; National Natural Science Foundation of China[62025604] ; National Natural Science Foundation of China[62206264] ; National Natural Science Foundation of China[92370102] ; Youth Innovation Promotion Association CAS ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB0680000] ; Innovation Funding of ICT, CAS[E000000]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:001364431200098
出版者	IEEE COMPUTER SOC
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/41093
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Xu, Qianqian; Huang, Qingming
作者单位	1.Chinese Acad Sci, Inst Informat Engn, State Key Lab Informat Secur SKLOIS, Beijing 100093, Peoples R China 2.Univ Chinese Acad Sci, Sch Cyber Secur, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 4.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 101408, Peoples R China 5.Alibaba Grp, Secur Dept, Hangzhou 311121, Peoples R China 6.Sun Yat Sen Univ, Sch Cyber Sci & Technol, Shenzhen Campus, Shenzhen 518107, Peoples R China 7.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Key Lab Big Data Min & Knowledge Management BDKM, Beijing 101408, Peoples R China 8.Peng Cheng Lab, Shenzhen 518055, Peoples R China
推荐引用方式 GB/T 7714	Bao, Shilong,Xu, Qianqian,Yang, Zhiyong,et al. Improved Diversity-Promoting Collaborative Metric Learning for Recommendation[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2024,46(12):9004-9022.
APA	Bao, Shilong,Xu, Qianqian,Yang, Zhiyong,He, Yuan,Cao, Xiaochun,&Huang, Qingming.(2024).Improved Diversity-Promoting Collaborative Metric Learning for Recommendation.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,46(12),9004-9022.
MLA	Bao, Shilong,et al."Improved Diversity-Promoting Collaborative Metric Learning for Recommendation".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 46.12(2024):9004-9022.