A Hybrid Music Recommendation System Combining Listening Habits and Tag Information (사용자 청취 습관과 태그 정보를 이용한 하이브리드 음악 추천 시스템)

Hyon Hee Kim (김현희); Kim，Dong-Geon (김동건); Cho Jin Nam (조진남)

A Hybrid Music Recommendation System Combining Listening Habits and Tag Information

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2013, 18(2), pp.107-116
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science

Hyon Hee Kim ¹, Kim，Dong-Geon ¹, Cho Jin Nam ¹

¹동덕여자대학교

Accredited

ABSTRACT

In this paper, we propose a hybrid music recommendation system combining users’ listening habits and tag information in a social music site. Most of commercial music recommendation systems recommend music items based on the number of plays and explicit ratings of a song.However, the approach has some difficulties in recommending new items with only a few ratings or recommending items to new users with little information. To resolve the problem, we use tag information which is generated by collaborative tagging. According to the meaning of tags, a weighted value is assigned as the score of a tag of an music item. By combining the score of tags and the number of plays, user profiles are created and collaborative filtering algorithm is executed.For performance evaluation, precision, recall, and F-measure are calculated using the listening habit-based recommendation, the tag score-based recommendation, and the hybrid recommendation, respectively. Our experiments show that the hybrid recommendation system outperforms the other two approaches.

KEYWORDS

Hybrid music recommendation, Listening habits, Tag Information

Citation status

* References for papers published after 2023 are currently being built.

[web] / Last.fm / http://www.last.fm/

[book] O. Celma / 2010 / Music Recommendation and Discovery / Springer

[confproc] P. Lamere / 2008 / In Proc. of International Conference on Music Information Retrieval / Journal of New Music Research 37(2) : 101~114

[journal] A. Nanopoulos / 2010 / MusicBox: Personalized Music Recommendation based on cubic Analysis of Social Tags / IEEE Trans. on Audio, Speech and Language Processing 18(2) : 1~7

[journal] 김현희 / 2012 / A Tag-based Music Recommendation Using UniTag Ontology / 한국컴퓨터정보학회논문지 / 한국컴퓨터정보학회 17(11) : 133~140

[journal] G. Adomavicius / 2005 / Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions / IEEE Trans. on Knowledge and Data Engineering 17(6) : 734~749

[journal] S. Jun / 2010 / Music Retrieval and Recommendation Scheme Based on Varying Mood Sequences / Int. J. on Semantic Web & Information Systems 6(2) : 1~16

[confproc] H. H. Kim / 2012 / Generation of Tag-based User Profiles for Clustering Users in a Social Music Site / In Proc. of Asian Conference on Intelligent Information and Database Systems : 51~61

[journal] S. Tan / 2011 / Using Rich Social Media Information for Music Recommendation via Hypergraph Model / ACM Trans. on Multimedia Computing, Communications and Applications 7(3) : 1~20

[journal] O. Celma / 2008 / Foafing the Music: Bridging the Semantic Gap in Music Recommendation / Web Semantics: Science, Services, and Agents on the World Wide Web 6(4) : 250~256

[web] / Pandora / http://www.pandora.com/

[journal] M. Kaminskas / 2012 / Contextual Music Information Retrieval and Recommendation: State of the Art and Challenges / Computer Science Review 6(2) : 89~119

[journal] 최창현 / 2009 / An Influence Value Algorithm based on Social Network in Knowledge Retrieval Service / 한국컴퓨터정보학회논문지 / 한국컴퓨터정보학회 14(10) : 43~53

[journal] 유석종 / 2012 / A Comprehensive Performance Evaluation in Collaborative Filtering / 한국컴퓨터정보학회논문지 / 한국컴퓨터정보학회 17(4) : 83~90

KJCKorea
Journal Central

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65