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Meta-Wrapper: Differentiable Wrapping Operator for User Interest Selection in CTR Prediction

文献类型：期刊论文

作者Cao, Tianwei4; Xu, Qianqian3; Yang, Zhiyong4; Huang, Qingming1,2,3
刊名IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
出版日期2022-11-01
卷号44期号:11页码:8449-8464
关键词Feature extraction Predictive models Wrapping Frequency modulation Computational modeling Training Recommender systems Click-through rate prediction recommender system bilevel optimization meta-learning
ISSN号0162-8828
DOI10.1109/TPAMI.2021.3103741
英文摘要Click-through rate (CTR) prediction, whose goal is to predict the probability of the user to click on an item, has become increasingly significant in the recommender systems. Recently, some deep learning models with the ability to automatically extract the user interest from his/her behaviors have achieved great success. In these work, the attention mechanism is used to select the user interested items in historical behaviors, improving the performance of the CTR predictor. Normally, these attentive modules can be jointly trained with the base predictor by using gradient descents. In this paper, we regard user interest modeling as a feature selection problem, which we call user interest selection. For such a problem, we propose a novel approach under the framework of the wrapper method, which is named Meta-Wrapper. More specifically, we use a differentiable module as our wrapping operator and then recast its learning problem as a continuous bilevel optimization. Moreover, we use a meta-learning algorithm to solve the optimization and theoretically prove its convergence. Meanwhile, we also provide theoretical analysis to show that our proposed method 1) efficiencies the wrapper-based feature selection, and 2) achieves better resistance to overfitting. Finally, extensive experiments on three public datasets manifest the superiority of our method in boosting the performance of CTR prediction.
资助项目National Key R&D Program of China[2018AAA0102003] ; National Natural Science Foundation of China[61931008] ; National Natural Science Foundation of China[61620106009] ; National Natural Science Foundation of China[61836002] ; National Natural Science Foundation of China[61976202] ; Fundamental Research Funds for the Central Universities ; Youth Innovation Promotion Association CAS ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB28000000] ; National Postdoctoral Program for Innovative Talents[BX2021298]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000864325900082
出版者IEEE COMPUTER SOC
源URL[http://119.78.100.204/handle/2XEOYT63/19774]  
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, Sch Comp Sci & Technol, Key Lab Big Data Min & Knowledge Management BDKM, Beijing 101408, 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
推荐引用方式
GB/T 7714		 Cao, Tianwei,Xu, Qianqian,Yang, Zhiyong,et al. Meta-Wrapper: Differentiable Wrapping Operator for User Interest Selection in CTR Prediction[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2022,44(11):8449-8464.
APA Cao, Tianwei,Xu, Qianqian,Yang, Zhiyong,&Huang, Qingming.(2022).Meta-Wrapper: Differentiable Wrapping Operator for User Interest Selection in CTR Prediction.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,44(11),8449-8464.
MLA Cao, Tianwei,et al."Meta-Wrapper: Differentiable Wrapping Operator for User Interest Selection in CTR Prediction".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 44.11(2022):8449-8464.

入库方式： OAI收割

来源：计算技术研究所

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