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Movie Recommendation using Unrated Data
Nie, D (Nie, Dong); Hong, LZ (Hong, Lingzi); Zhu, TS (Zhu, Tingshao)
第一作者Nie, D (Nie, Dong)
2013
会议名称12th International Conference on Machine Learning and Applications (ICMLA)
通讯作者邮箱tszhu@psych.ac.cn
会议录名称2013 12TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2013)
卷号1
期号不详
页码344-347
会议日期DEC 04-07, 2013
会议地点Miami, FL
摘要

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.

学科门类Computer Science
语种英语
文献类型会议论文
条目标识符http://ir.psych.ac.cn/handle/311026/26524
专题社会与工程心理学研究室
作者单位Chinese Acad Sci, Univ Chinese Acad Sci, Inst Psychol, Beijing , Peoples R China.
推荐引用方式
GB/T 7714
Nie, D ,Hong, LZ ,Zhu, TS . Movie Recommendation using Unrated Data[C],2013:344-347.
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