TY - GEN
T1 - Music Recommendation Based on Information of User Profiles, Music Genres and User Ratings
AU - Su, Ja Hwung
AU - Chin, Chu Yu
AU - Yang, Hsiao Chuan
AU - Tseng, Vincent S.
AU - Hsieh, Sun Yuan
N1 - Funding Information:
Acknowledgement. This research was supported by Ministry of Science and Technology, Taiwan, R.O.C. under grant no. MOST 105-2221-E-230-011-MY2 and MOST 106-2632-S-424-001.
Publisher Copyright:
© 2018, Springer International Publishing AG, part of Springer Nature.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
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U2 - 10.1007/978-3-319-75417-8_50
DO - 10.1007/978-3-319-75417-8_50
M3 - Conference contribution
AN - SCOPUS:85043497928
SN - 9783319754161
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 528
EP - 538
BT - Intelligent Information and Database Systems - 10th Asian Conference, ACIIDS 2018, Proceedings
A2 - Pham, Hoang
A2 - Nguyen, Ngoc Thanh
A2 - Trawinski, Bogdan
A2 - Hoang, Duong Hung
A2 - Hong, Tzung-Pei
PB - Springer Verlag
T2 - 10th International scientific conferences on research and applications in the field of intelligent information and database systems, ACIIDS 2018
Y2 - 19 March 2018 through 21 March 2018
ER -