標題: Music Recommendation Based on Information of User Profiles, Music Genres and User Ratings
作者: Su, Ja-Hwung
Chin, Chu-Yu
Yang, Hsiao-Chuan
Tseng, Vincent S.
Hsieh, Sun-Yuan
資訊工程學系
Department of Computer Science
關鍵字: Collaborative filtering;Music recommendation;New user;Rating sparsity;User-based
公開日期: 1-一月-2018
摘要: Music data has been becoming bigger and bigger in recent years. It makes online music stores hard to provide the users with good personalized services. Therefore, a number of past studies were proposed for effectively retrieving the user preferences on music. However, they countered problems such as new user, new item and rating sparsity. To cope with these problems, in this paper, we propose a creative method that integrates information of user profiles, music genres and user ratings. In terms of solving problem of new user, the user similarities can be calculated by the profiles instead of ratings. By the user similarities, the unknown ratings can be predicted using user-based Collaborative Filtering. In terms of solving problem of rating sparsity, the unknown ratings are initialized by ratings of music genres. Even facing new music items, the rating data will not be sparse due to imputing the initialized ratings. Because the rating data is enriched, the user preference can be retrieved by item-based Collaborative Filtering. The experimental results reveal that, our proposed method performs more promising than the compared methods in terms of Root Mean Squared Error.
URI: http://dx.doi.org/10.1007/978-3-319-75417-8_50
http://hdl.handle.net/11536/146162
ISSN: 0302-9743
DOI: 10.1007/978-3-319-75417-8_50
期刊: INTELLIGENT INFORMATION AND DATABASE SYSTEMS, ACIIDS 2018, PT I
Volume: 10751
起始頁: 528
結束頁: 538
顯示於類別:會議論文