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Learning to Learn a Cold-start Sequential Recommender

文献类型：期刊论文

作者Huang, Xiaowen4,5; Sang, Jitao4,5; Yu, Jian4,5; Xu, Changsheng1,2,3
刊名ACM TRANSACTIONS ON INFORMATION SYSTEMS
出版日期2022-04-01
卷号40期号:2页码:25
ISSN号1046-8188
关键词Cold-start recommendation meta-learning graph representation sequential recommendation
DOI10.1145/3466753
通讯作者Xu, Changsheng(csxu@nlpria.ac.cn)
英文摘要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.
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]
WOS研究方向Computer Science
语种英语
出版者ASSOC COMPUTING MACHINERY
WOS记录号WOS:000752408800010
资助机构National Key R&D Program of China ; Fundamental Research Funds for the Central Universities ; Beijing Natural Science Foundation ; National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/47629]  
专题自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队
通讯作者Xu, Changsheng
作者单位1.Peng Cheng Lab, Shenzhen, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, 80 Zhongguancun Rd, Beijing, Peoples R China
3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, 95 Zhonggumicun Rd, Beijing, Peoples R China
4.Beijing Jiaotong Univ, Beijing Key Lab Traff Data Anal & Min, Beijing, Peoples R China
5.Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing, Peoples R China
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.

入库方式： OAI收割

来源：自动化研究所

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