Neural Collaborative Preference Learning With Pairwise Comparisons

doi:10.1109/TMM.2020.3006373

	Neural Collaborative Preference Learning With Pairwise Comparisons
	Li, Zhaopeng 1,2,3; Xu, Qianqian 4; Jiang, Yangbangyan 2,3; Ma, Ke 5,6; Cao, Xiaochun 3,6,7; Huang, Qingming 4,5,8,9
	2021
发表期刊	IEEE TRANSACTIONS ON MULTIMEDIA
ISSN	1520-9210
卷号	23 页码:1977-1989
摘要	Collaborative Ranking (CR), as an effective recommendation framework, has attracted increasing attention in recent years. Most CR methods simply adopt the inner product between user/item embeddings as the rating score function, with an assumption that the interacted items are preferred to non-interacted ones. However, such fixed score functions and assumptions might not be sufficient to capture the real preference ranking list from the complicated interactions in real-world data. To alleviate this issue, we develop a novel collaborative ranking framework that learns an arbitrary utility function for item ranking with user preference concerned. In the core of our framework, a neural network is employed to model the utility function for personalized ranking with the strength of its nonlinearity. On top of this, we further adopt a pairwise ranking loss for user-item pairs to preserve the preference order of items for users. Besides, such a utility function enables us to generate the final top-K preference list in a much easier way. Finally, extensive experiments on four real-world datasets show the validity of our proposed method.
关键词	Recommender system collaborative ranking neural networks preference ranking
DOI	10.1109/TMM.2020.3006373
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[61620106009] ; National Natural Science Foundation of China[U1636214] ; National Natural Science Foundation of China[61931008] ; National Natural Science Foundation of China[61836002] ; National Natural Science Foundation of China[61971016] ; National Natural Science Foundation of China[U1936208] ; National Natural Science Foundation of China[61672514] ; National Natural Science Foundation of China[61976202] ; Key Research Program of Frontier Sciences, CAS[QYZDJ-SSW-SYS013] ; Beijing Education Committee Cooperation Beijing Natural Science Foundation[KZ201910005007] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB28000000] ; Beijing Natural Science Foundation[4182079] ; Youth Innovation Promotion Association CAS
WOS研究方向	Computer Science ; Telecommunications
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications
WOS记录号	WOS:000724477100005
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
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/2XEOYT63/18152
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Huang, Qingming
作者单位	1.Chinese Acad Sci, Inst Informat Engn, State Key Lab Informat Secur, SKLOIS, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Informat Engn, State Key Lab Informat Secur SKLOIS, Beijing 100093, Peoples R China 3.Univ Chinese Acad Sci, Sch Cyber Secur, Beijing 100049, 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 Comp Sci & Technol, Beijing 100049, Peoples R China 6.Peng Cheng Lab, Artificial Intelligence Res Ctr, Shenzhen 518055, Peoples R China 7.Chinese Acad Sci, Inst Informat Engn, Key Lab Informat Secur, Beijing 100093, Peoples R China 8.Univ Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management BDKM, Beijing 101408, Peoples R China 9.Peng Cheng Lab, Shenzhen 518055, Peoples R China
推荐引用方式 GB/T 7714	Li, Zhaopeng,Xu, Qianqian,Jiang, Yangbangyan,et al. Neural Collaborative Preference Learning With Pairwise Comparisons[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2021,23:1977-1989.
APA	Li, Zhaopeng,Xu, Qianqian,Jiang, Yangbangyan,Ma, Ke,Cao, Xiaochun,&Huang, Qingming.(2021).Neural Collaborative Preference Learning With Pairwise Comparisons.IEEE TRANSACTIONS ON MULTIMEDIA,23,1977-1989.
MLA	Li, Zhaopeng,et al."Neural Collaborative Preference Learning With Pairwise Comparisons".IEEE TRANSACTIONS ON MULTIMEDIA 23(2021):1977-1989.