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Movie Recommendation using Unrated Data
Nie, D (Nie, Dong); Hong, LZ (Hong, Lingzi); Zhu, TS (Zhu, Tingshao)
First AuthorNie, D (Nie, Dong)
2013
Conference Name12th International Conference on Machine Learning and Applications (ICMLA)
Correspondent Emailtszhu@psych.ac.cn
Source Publication2013 12TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2013)
Volume1
Issue不详
Pages344-347
Conference DateDEC 04-07, 2013
Conference PlaceMiami, FL
Abstract

Model based movie recommender systems have been thoroughly investigated in the past few years, and they rely on rating data. In this paper, we take into account unrated data of genre information to improve the performance of movie recommendation. We propose a novel method to measure users' preference on movie genres, and use Pearson Correlation Coefficient (PCC) to compute the user similarity. A matrix factorization framework is introduced for genre preference regularization. Experimental results on MovieLens data set demonstrate that the approach performs well. Our method can also be used to increase the genre diversity of recommendations to some extent.

MOST Discipline CatalogueComputer Science
Language英语
Document Type会议论文
Identifierhttp://ir.psych.ac.cn/handle/311026/26524
Collection社会与工程心理学研究室
AffiliationChinese Acad Sci, Univ Chinese Acad Sci, Inst Psychol, Beijing , Peoples R China.
Recommended Citation
GB/T 7714
Nie, D ,Hong, LZ ,Zhu, TS . Movie Recommendation using Unrated Data[C],2013:344-347.
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