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Personalized ranking with pairwise Factorization Machines
Guo, Weiyu1,2; Wu, Shu1; Wang, Liang1; Tan, Tieniu1
Source PublicationNEUROCOMPUTING
AbstractPairwise 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.
KeywordPersonalized Ranking Adaptive Sampling Pairwise Learning
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
Funding OrganizationNational Basic Research Program of China(2012CB316300) ; National Natural Science Foundation of China(61403390 ; U1435221)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000386741300020
Citation statistics
Cited Times:5[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Corresponding AuthorWu, Shu
Affiliation1.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
Recommended Citation
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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