Paper Info
Title
A Personalized Music Recommender System Using User Contents, Music Contents And Preference Ratings
Abstract
Recently, the advances in communication technologies have made music retrieval easier. Without downloading the music, the users can listen to music through online music websites. This incurs a challenging issue of how to provide the users with an effective online listening service. Although a number of past studies paid attention to this issue, the problems of new user, new item and rating sparsity are not easy to solve. To deal with these problems, in this paper, we propose a novel music recommender system that fuses user contents, music contents and preference ratings to enhance the music recommendation. For dealing with problem of new user, the user similarities are calculated by user profiles instead of traditional ratings. By the user similarities, the unknown ratings can be predicted using user-based Collaborative Filtering (CF). For dealing with problems of rating sparsity and new items, the unknown ratings are initialized by acoustic features and music genre ratings. Because the unknown ratings are initially imputed, the rating data will be enriched. Thereupon, the user preference can be predicted effectively by item-based CF. The evaluation results show that our proposed music recommender system performs better than the state-of-the-arts methods in terms of Root Mean Squared Error.
Year
DOI
Venue
2020
10.1142/S2196888820500049
VIETNAM JOURNAL OF COMPUTER SCIENCE
Keywords
DocType
Volume
Collaborative filtering, music recommendation, new user, rating sparsity, user-based
Journal
7
Issue
ISSN
Citations 
1
2196-8888
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Ja-Hwung Su132924.53
Chu-Yu Chin200.34
Yi-Wen Liao300.34
Hsiao-Chuan Yang400.68
Vincent S. Tseng52923161.33
Sun-Yuan Hsieh61715112.85