Learning to Learn a Cold-start Sequential Recommender

doi:10.1145/3466753

	Learning to Learn a Cold-start Sequential Recommender
	Huang, Xiaowen 1,2; Sang, Jitao 1,2; Yu, Jian 1,2; Xu, Changsheng3,4,5
发表期刊	ACM TRANSACTIONS ON INFORMATION SYSTEMS
ISSN	1046-8188
	2022-04-01
卷号	40 期号:2 页码:25
通讯作者	Xu, Changsheng(csxu@nlpria.ac.cn)
摘要	The cold-start recommendation is an urgent problem in contemporary online applications. It aims to provide users whose behaviors are literally sparse with as accurate recommendations as possible. Many data-driven algorithms, such as the widely used matrix factorization, underperform because of data sparseness. This work adopts the idea of meta-learning to solve the user's cold-start recommendation problem. We propose a meta-learning-based cold-start sequential recommendation framework called metaCSR, including three main components: Diffusion Representer for learning better user/item embedding through information diffusion on the interaction graph; Sequential Recommender for capturing temporal dependencies of behavior sequences; and Meta Learner for extracting and propagating transferable knowledge of prior users and learning a good initialization for new users. metaCSR holds the ability to learn the common patterns from regular users' behaviors and optimize the initialization so that the model can quickly adapt to new users after one or a few gradient updates to achieve optimal performance. The extensive quantitative experiments on three widely used datasets show the remarkable performance of metaCSR in dealing with the user cold-start problem. Meanwhile, a series of qualitative analysis demonstrates that the proposed metaCSR has good generalization.
关键词	Cold-start recommendation meta-learning graph representation sequential recommendation
DOI	10.1145/3466753
收录类别	SCI
语种	英语
资助项目	National Key R&D Program of China[2018AAA0100604] ; Fundamental Research Funds for the Central Universities[2021RC217] ; Beijing Natural Science Foundation[JQ20023] ; National Natural Science Foundation of China[61632002] ; National Natural Science Foundation of China[61832004] ; National Natural Science Foundation of China[62036012] ; National Natural Science Foundation of China[61720106006]
项目资助者	National Key R&D Program of China ; Fundamental Research Funds for the Central Universities ; Beijing Natural Science Foundation ; National Natural Science Foundation of China
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems
WOS记录号	WOS:000752408800010
出版者	ASSOC COMPUTING MACHINERY
七大方向——子方向分类	推荐系统
引用统计	被引频次：7[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/47630
专题	多模态人工智能系统全国重点实验室_多媒体计算
通讯作者	Xu, Changsheng
作者单位	1.Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing, Peoples R China 2.Beijing Jiaotong Univ, Beijing Key Lab Traff Data Anal & Min, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, 95 Zhonggumicun Rd, Beijing, Peoples R China 4.Univ Chinese Acad Sci, Sch Artificial Intelligence, 80 Zhongguancun Rd, Beijing, Peoples R China 5.Peng Cheng Lab, Shenzhen, Peoples R China
通讯作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Huang, Xiaowen,Sang, Jitao,Yu, Jian,et al. Learning to Learn a Cold-start Sequential Recommender[J]. ACM TRANSACTIONS ON INFORMATION SYSTEMS,2022,40(2):25.
APA	Huang, Xiaowen,Sang, Jitao,Yu, Jian,&Xu, Changsheng.(2022).Learning to Learn a Cold-start Sequential Recommender.ACM TRANSACTIONS ON INFORMATION SYSTEMS,40(2),25.
MLA	Huang, Xiaowen,et al."Learning to Learn a Cold-start Sequential Recommender".ACM TRANSACTIONS ON INFORMATION SYSTEMS 40.2(2022):25.