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

文献类型：期刊论文

作者Bao, Shilong5,6; Xu, Qianqian4,5; Yang, Zhiyong3; Cao, Xiaochun6; Huang, Qingming1,2,4
刊名IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
出版日期2023
卷号45期号:1页码:1017-1035
关键词Recommendation system collaborative metric learning negative sampling machine learning
ISSN号0162-8828
DOI10.1109/TPAMI.2022.3141095
英文摘要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.
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
语种英语
WOS记录号WOS:000899419900064
出版者IEEE COMPUTER SOC
源URL[http://119.78.100.204/handle/2XEOYT63/20148]  
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.Peng Cheng Lab, Shenzhen 518055, Peoples R China
2.Univ Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management BDKM, Sch Comp Sci & Technol, Beijing 101408, Peoples R China
3.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 101408, Peoples R China
4.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
5.Univ Chinese Acad Sci, Sch Cyber Secur, Beijing 100049, Peoples R China
6.Chinese Acad Sci, Inst Informat Engn, State Key Lab Informat Secur SKLOIS, Beijing 100093, 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.

入库方式： OAI收割

来源：计算技术研究所

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