Extracting Latent Variables Related to User Preferences on Genres in Yahoo! Music Ratings Data
Chang, Xiaowen; Bokhari, Ehsan
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https://hdl.handle.net/2142/77703
Description
Title
Extracting Latent Variables Related to User Preferences on Genres in Yahoo! Music Ratings Data
Author(s)
Chang, Xiaowen
Bokhari, Ehsan
Contributor(s)
Bokhari, Ehsan
Issue Date
2015-05
Keyword(s)
Statistics
Multidimensional Scaling
Principal Component Analysis
Nonnegative Matrix Factorization
Yahoo! Music
Latent Variables
Abstract
The objective of the study is to learn community-based user preferences and identify user tastes in music via Yahoo! Music User ratings. The dataset consists of over seven million ratings of 136 thousand songs given by 1.8 million users. And it is characterized by artist, album and genre attributes. In our study, we only used a subset of this raw dataset which contains the average ratings for 4,640 users across seven popular and representative genres. We conducted multidimensional scaling, principal component analysis and nonnegative matrix factorization to extract latent variables related to user preferences on different genres.
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