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Personalized ranking with pairwise Factorization Machines
Guo, Weiyu1,2; Wu, Shu1; Wang, Liang1; Tan, Tieniu1
2016-11-19
发表期刊NEUROCOMPUTING
卷号214期号:null页码:191-200
文章类型Article
摘要Pairwise learning is a vital technique for personalized ranking with implicit feedback. Given the assumption that each user is more interested in items which have been previously selected by the user than the remaining ones, pairwise learning algorithms can well learn users' preference, from not only the observed user feedbacks but also the underlying interactions between users and items. However, a mass of training instances are randomly derived according to such assumption, which makes the learning procedure often converge slowly and even result in poor predictive models. In addition, the cold start problem often perplexes pairwise learning methods, since most of traditional methods in personalized ranking only take explicit ratings or implicit feedbacks into consideration. For dealing with the above issues, this work proposes a novel personalized ranking model which incorporates implicit feedback with content information by making use of Factorization Machines. For efficiently estimating the parameters of the proposed model, we develop an adaptive sampler to draw informative training instances based on content information of users and items. The experimental results show that our adaptive item sampler indeed can speed up our model, and our model outperforms advanced methods in personalized ranking. (C) 2016 Elsevier B.V. All rights reserved.
关键词Personalized Ranking Adaptive Sampling Pairwise Learning
WOS标题词Science & Technology ; Technology
DOI10.1016/j.neucom.2016.05.074
收录类别SCI
语种英语
项目资助者National Basic Research Program of China(2012CB316300) ; National Natural Science Foundation of China(61403390 ; U1435221)
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000386741300020
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/12313
专题智能感知与计算研究中心
通讯作者Wu, Shu
作者单位1.Chinese Acad Sci, Ctr Res Intelligent Percept & Comp, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714
Guo, Weiyu,Wu, Shu,Wang, Liang,et al. Personalized ranking with pairwise Factorization Machines[J]. NEUROCOMPUTING,2016,214(null):191-200.
APA Guo, Weiyu,Wu, Shu,Wang, Liang,&Tan, Tieniu.(2016).Personalized ranking with pairwise Factorization Machines.NEUROCOMPUTING,214(null),191-200.
MLA Guo, Weiyu,et al."Personalized ranking with pairwise Factorization Machines".NEUROCOMPUTING 214.null(2016):191-200.
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