Rethinking Collaborative Metric Learning: Toward an Efficient Alternative Without Negative Sampling

doi:10.1109/TPAMI.2022.3141095

	Rethinking Collaborative Metric Learning: Toward an Efficient Alternative Without Negative Sampling
	Bao, Shilong 1,2; Xu, Qianqian 2,3; Yang, Zhiyong 4; Cao, Xiaochun 1; Huang, Qingming 3,5,6
	2023
发表期刊	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
ISSN	0162-8828
卷号	45 期号:1 页码:1017-1035
摘要	The recently proposed Collaborative Metric Learning (CML) paradigm has aroused wide interest in the area of recommendation systems (RS) owing to its simplicity and effectiveness. Typically, the existing literature of CML depends largely on the negative sampling strategy to alleviate the time-consuming burden of pairwise computation. However, in this work, by taking a theoretical analysis, we find that negative sampling would lead to a biased estimation of the generalization error. Specifically, we show that the sampling-based CML would introduce a bias term in the generalization bound, which is quantified by the per-user Total Variance (TV) between the distribution induced by negative sampling and the ground truth distribution. This suggests that optimizing the sampling-based CML loss function does not ensure a small generalization error even with sufficiently large training data. Moreover, we show that the bias term will vanish without the negative sampling strategy. Motivated by this, we propose an efficient alternative without negative sampling for CML named Sampling-Free Collaborative Metric Learning (SFCML), to get rid of the sampling bias in a practical sense. Finally, comprehensive experiments over seven benchmark datasets speak to the supriority of the proposed algorithm.
关键词	Recommendation system collaborative metric learning negative sampling machine learning
DOI	10.1109/TPAMI.2022.3141095
收录类别	SCI
语种	英语
资助项目	National Key R&D Program of China[2018AAA0102003] ; National Natural Science Foundation of China[U21B2038] ; National Natural Science Foundation of China[U2001202] ; National Natural Science Foundation of China[U1936208] ; National Natural Science Foundation of China[61620106009] ; National Natural Science Foundation of China[62025604] ; National Natural Science Foundation of China[61931008] ; National Natural Science Foundation of China[6212200758] ; National Natural Science Foundation of China[61976202] ; Fundamental Research Funds for the Central Universities ; National Postdoctoral Program for Innovative Talents[BX2021298] ; Youth Innovation Promotion Association CAS ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB28000000]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000899419900064
出版者	IEEE COMPUTER SOC
引用统计	被引频次：6[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/2XEOYT63/20149
专题	中国科学院计算技术研究所期刊论文
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.Univ Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management BDKM, Sch Comp Sci & Technol, Beijing 101408, Peoples R China 6.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. Rethinking Collaborative Metric Learning: Toward an Efficient Alternative Without Negative Sampling[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2023,45(1):1017-1035.
APA	Bao, Shilong,Xu, Qianqian,Yang, Zhiyong,Cao, Xiaochun,&Huang, Qingming.(2023).Rethinking Collaborative Metric Learning: Toward an Efficient Alternative Without Negative Sampling.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,45(1),1017-1035.
MLA	Bao, Shilong,et al."Rethinking Collaborative Metric Learning: Toward an Efficient Alternative Without Negative Sampling".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 45.1(2023):1017-1035.