research-article

A novel method for personalized music recommendation

Authors:

Vincent S. TsengAuthors Info & Claims

Expert Systems with Applications: An International Journal, Volume 36, Issue 6

Pages 10035 - 10044

https://doi.org/10.1016/j.eswa.2009.01.074

Published: 01 August 2009 Publication History

Abstract

With the development of digital music technologies, it is an interesting and useful issue to recommend the 'favored music' from large amounts of digital music. Some Web-based music stores can recommend popular music which has been rated by many people. However, three problems that need to be resolved in the current methods are: (a) how to recommend the 'favored music' which has not been rated by anyone, (b) how to avoid repeatedly recommending the 'disfavored music' for users, and (c) how to recommend more interesting music for users besides the ones users have been used to listen. To achieve these goals, we proposed a novel method called personalized hybrid music recommendation, which combines the content-based, collaboration-based and emotion-based methods by computing the weights of the methods according to users' interests. Furthermore, to evaluate the recommendation accuracy, we constructed a system that can recommend the music to users after mining users' logs on music listening records. By the feedback of the user's options, the proposed methods accommodate the variations of the users' musical interests and then promptly recommend the favored and more interesting music via consecutive recommendations. Experimental results show that the recommendation accuracy achieved by our method is as good as 90%. Hence, it is helpful for recommending the 'favored music' to users, provided that each music object is annotated with the related music emotions. The framework in this paper could serve as a useful basis for studies on music recommendation.

References

[1]

Amazon. .

[2]

Chai, W., & Vercoe, B. (2000). Using user models in music information retrieval systems. In Proceedings of international symposium on music information retrieval.

[3]

Chen, H. C., & Chen, A. L. P. (2001). Music recommendation system based on music data grouping and user interests. In Proceedings of the ACM international conference on information and knowledge management.

[4]

Dixon, S., Bainbridge, D., & Typke, R. (2007). Sociology and music recommendation systems. In Proceedings of the international symposium on music information retrieval.

[5]

DownWithTheLoads. .

[6]

Folksongs. .

[7]

iTunes. .

[8]

Karydis, I., Nanopoulos, A., Papadopoulos, A., Katsaros, D., & Manolopoulos, Y. (2005). Content-based music information retrieval in wireless ad-hoc networks. In Proceedings of the 6th symposium on music information retrieval.

[9]

KIDiddles. .

[10]

C.L. Krumhansl, Music: A link between cognition and emotion, Current Directions in Psychological Science, 11 (2002) 45-50.

[11]

Kuo, F. F., & Shan, M. K. (2002). A personalized music filtering system based on melody style classification. In Proceedings of the IEEE international conference on data mining.

[12]

Kuo, F. F., Chiang, M. F., Shan, M. K., & Lee, S. Y. (2005). Emotion-based music recommendation based on association discovery from film music. In Proceedings of the ACM international conference on multimedia.

[13]

W.P. Lee, C.H. Liu, C.C. Lu, Intelligent agent-based systems for personalized recommendations in internet commerce, Expert Systems with Applications, 22 (2002) 275-284.

[14]

W.P. Lee, C.C. Lu, Customising WAP-based information services on mobile networks, Personal and Ubiquitous Computing, 7 (2003) 321-330.

Digital Library

[15]

C.C. Liu, J.L. Hsu, A.L.P. Chen, Discovering nontrivial repeating patterns in music data, IEEE Transactions on Multimedia, 3 (2001) 311-325.

Digital Library

[16]

Lu, C. C., & Tseng, V. S. (2006). Music classification by parsing main melody. In Proceedings of the international computer symposium (pp. 281-285).

[17]

L. Lu, D. Liu, H.J. Zhang, Automatic mood detection and tracking of music audio signals, IEEE Transactions on Audio, Speech, and Language Processing, 14 (2006) 5-18.

Digital Library

[18]

Meldex. .

[19]

MIDI Database. .

[20]

MIDI Manufacturers Association. .

[21]

Miller, H. M., & Williams, E. (1991). Introduction to music. Perennial Press.

[22]

Recordare. .

[23]

Reilly, W. S. N. (1996). Believable social and emotion agents. Ph.D. dissertation, Carnegie Mellon University.

[24]

Sadie, S., & Latham, A. (1990). The cambridge music guide. Cambridge University Press.

[25]

Shardan, U., & Maes, P. (1995). Social information filtering: Algorithms for automating word of mouth. In Proceedings of the conference on human factors in computing systems.

[26]

R.E. Thayer, The biopsychology of mood and arousal, Oxford University Press, 1989.

[27]

Themefinder. .

[28]

Tiemann, M., Pauws, S., & Vignoli, F. (2007). Ensemble learning for hybrid music recommendation. In Proceedings of the international symposium on music information retrieval.

[29]

Tseng, Y. H. (1999). Content-based retrieval for music collections. In Proceedings of the 22nd international ACM SIGIR conference on research and development in information retrieval (pp. 176-182).

[30]

Tseng, V. S., Su, J. H., & Huang, J. H. (2006). A novel video annotation method by integrating visual features and frequent patterns. In ACM KDD workshop on multimedia data mining, Philadelphia, USA.

[31]

Tseng, V. S., Su, J. H., Wang, B. W., & Lin, Y. M. (2007). Web image annotation by fusing visual features and textual information. In ACM symposium on applied computing (SAC), Korea.

[32]

Typke, R., Wiering, F., & Veltkamp, R. C. (2005). A survey of music information retrieval systems. In Proceedings of the 6th symposium on music information retrieval (pp. 153-160).

[33]

Uitdenbogerd, A., & Schyndel, R. (2002). A review of factors affecting music recommender success. In Proceedings of the international symposium on music information retrieval.

[34]

Wang, M. Y., Zhang, N. Y., & Zhu, H. C. (2004). User-adaptive music emotion recognition. In Proceedings of the 7th international conference on signal processing (pp. 1352-1355).

[35]

Weyde, T., & Datzko, C. (2005). Efficient melody retrieval with motif contour classes. In Proceedings of the 6th symposium on music information retrieval (pp. 686-689).

[36]

Yang, D., & Lee, W. S. (2004). Disambiguating music emotion using software agents. In Proceedings of the international symposium on music information retrieval (pp. 218-223).

[37]

Yeh, C. C., Tseng, S. S., Tsai, P.C., & Weng, J. F. (2006). Building a personalized music emotion prediction system. In Proceedings of the 7th Pacific rim conference on multimedia (pp. 730-739).

Cited By

Sánchez-Moreno DLópez Batista VMuñoz Vicente MGil González AMoreno-García M(2020)A Session-Based Song Recommendation Approach Involving User Characterization along the Play Power-Law DistributionComplexity10.1155/2020/73094532020Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1155/2020/7309453
Roy SBiswas MDe D(2020)iMusic: a session-sensitive clustered classical music recommender system using contextual representation learningMultimedia Tools and Applications10.1007/s11042-020-09126-879:33-34(24119-24155)Online publication date: 1-Sep-2020
https://dl.acm.org/doi/10.1007/s11042-020-09126-8
Sánchez-Moreno DMoreno-García MSonboli NMobasher BBurke R(2018)Inferring User Expertise from Social Tagging in Music Recommender Systems for Streaming ServicesHybrid Artificial Intelligent Systems10.1007/978-3-319-92639-1_4(39-49)Online publication date: 20-Jun-2018
https://dl.acm.org/doi/10.1007/978-3-319-92639-1_4
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A novel method for personalized music recommendation
1. Applied computing
  1. Arts and humanities

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Copyright © Elsevier Ltd.

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Pergamon Press, Inc.

United States

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Published: 01 August 2009

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Cited By

Sánchez-Moreno DLópez Batista VMuñoz Vicente MGil González AMoreno-García M(2020)A Session-Based Song Recommendation Approach Involving User Characterization along the Play Power-Law DistributionComplexity10.1155/2020/73094532020Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1155/2020/7309453
Roy SBiswas MDe D(2020)iMusic: a session-sensitive clustered classical music recommender system using contextual representation learningMultimedia Tools and Applications10.1007/s11042-020-09126-879:33-34(24119-24155)Online publication date: 1-Sep-2020
https://dl.acm.org/doi/10.1007/s11042-020-09126-8
Sánchez-Moreno DMoreno-García MSonboli NMobasher BBurke R(2018)Inferring User Expertise from Social Tagging in Music Recommender Systems for Streaming ServicesHybrid Artificial Intelligent Systems10.1007/978-3-319-92639-1_4(39-49)Online publication date: 20-Jun-2018
https://dl.acm.org/doi/10.1007/978-3-319-92639-1_4
Chen YCheng CChen DLai C(2016)A mood- and situation-based model for developing intuitive Pop music recommendation systemsExpert Systems: The Journal of Knowledge Engineering10.1111/exsy.1213233:1(77-91)Online publication date: 1-Feb-2016
https://dl.acm.org/doi/10.1111/exsy.12132
Sánchez-Moreno DGil González AMuñoz Vicente MLópez Batista VMoreno García M(2016)A collaborative filtering method for music recommendation using playing coefficients for artists and usersExpert Systems with Applications: An International Journal10.1016/j.eswa.2016.09.01966:C(234-244)Online publication date: 30-Dec-2016
https://dl.acm.org/doi/10.1016/j.eswa.2016.09.019
Deng JLeung CMilani AChen L(2015)Emotional States Associated with MusicACM Transactions on Interactive Intelligent Systems10.1145/27235755:1(1-36)Online publication date: 25-Mar-2015
https://dl.acm.org/doi/10.1145/2723575
Liu WWu CFeng BLiu J(2015)Conditional preference in recommender systemsExpert Systems with Applications: An International Journal10.1016/j.eswa.2014.08.04442:2(774-788)Online publication date: 1-Feb-2015
https://dl.acm.org/doi/10.1016/j.eswa.2014.08.044
Hyung ZLee KLee K(2014)Music recommendation using text analysis on song requests to radio stationsExpert Systems with Applications: An International Journal10.1016/j.eswa.2013.10.03541:5(2608-2618)Online publication date: 1-Apr-2014
https://dl.acm.org/doi/10.1016/j.eswa.2013.10.035
Bogdanov DHaro MFuhrmann FXambó AGóMez EHerrera P(2013)Semantic audio content-based music recommendation and visualization based on user preference examplesInformation Processing and Management: an International Journal10.1016/j.ipm.2012.06.00449:1(13-33)Online publication date: 1-Jan-2013
https://dl.acm.org/doi/10.1016/j.ipm.2012.06.004
Guo YWu CPeteiro-Barral D(2012)An EEG-Based brain informatics application for enhancing music experienceProceedings of the 2012 international conference on Brain Informatics10.1007/978-3-642-35139-6_25(265-276)Online publication date: 4-Dec-2012
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