Call for Paper - January 2023 Edition
IJCA solicits original research papers for the January 2023 Edition. Last date of manuscript submission is December 20, 2022. Read More

A Novel Method for Music Recommendation using Social Media Tags

Print
PDF
International Journal of Computer Applications
© 2015 by IJCA Journal
Volume 122 - Number 2
Year of Publication: 2015
Authors:
Gunjan Advani
Neha Soni
10.5120/21676-4765

Gunjan Advani and Neha Soni. Article: A Novel Method for Music Recommendation using Social Media Tags. International Journal of Computer Applications 122(2):37-43, July 2015. Full text available. BibTeX

@article{key:article,
	author = {Gunjan Advani and Neha Soni},
	title = {Article: A Novel Method for Music Recommendation using Social Media Tags},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {122},
	number = {2},
	pages = {37-43},
	month = {July},
	note = {Full text available}
}

Abstract

Tremendous growth of online music data has given new opportunities for building more effective music recommender systems. These systems help users to find and categorize songs according to their likings. The main goal of Recommender Systems (RS) is to predict ratings of items that the users would be interested in. With the rapid development of the Collaborative Tagging approach, tags could be fascinating and helpful information to enrich RS systems. Attributes are the "global" depictions of items while tags are "local" depictions of items provided by the users. Explicit feedback and implicit feedback demonstrates distinct properties of users' preferences with both advantages and disadvantages. Combination of these in a user preference model not only exhibits a number of disputes but can also overwhelm the problems related with each other. Hence, to take advantage of tagging data and see whether better recommendations are generated or not, a novel method for music recommendation is proposed that combines implicit feedback and explicit feedback of the users. Also, both explicit types of feedbacks are normalized before transformation into ratings in order to provide the desired ratings in case of skewed play counts data.

References

  • Marius Kaminskas and Francesco Ricci. "Contextual music information retrieval and recommendation: State of the art and challenges". Elsevier, 2012.
  • Yading Song, Simon Dixon, and Marcus Pearce. "A Survey of Music Recommendation Systems and Future Perspectives". 9th International Symposium on CMMR,2012.
  • Alexandra Uitdenbogerd and van Schyndel Ron. A Review of Factors Affecting Music Recommender. In 3rd International Conference on Music Information Retrieval (2002), 2002
  • F. Ricci, L. Rokach, B. Shapira, P. B. Kantor (Eds. ), Recommender Systems Handbook, Springer, 2011.
  • http://www. last. fm
  • Robin Burke. Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction, 12(4):331–370, 2002.
  • Dmitry Bogdanov and Perfecto Herrera. How Much Metadata Do We Need in Music Recommnendation? A Subjective Evaluation Using Preference Sets. In 12th International Society for Music Information Retrieval Conference, number ISMIR 2011, pages 97–102, 2011.
  • G. Adomavicius, B. Mobasher, F. Ricci, A. Tuzhilin, Contextaware recommender systems, AI Magazine 32 (3) (2011) 67–80.
  • J. Y. Kim, N. J. Belkin, Categories of music description and search terms and phrases used by non-music experts, in: 3rd International Conference on Music Information Retrieval, Paris, France, 2002, pp. 209–214.
  • Tso-Sutter K. H. L, Marinho L. B. and Schmidt-Thieme L. "Tag-aware recommender systems by fusion of collaborative filtering algorithms". Proc. Int. Conf. on the ACM symposium on Applied computing, Fortaleza, pp. 1995-1999, 2008.
  • Gunjan Advani and Neha Soni, "A Novel Way for Personalized Music Recommendation Using Social Media Tags", IJSRD - International Journal for Scientific Research & Development ,Vol. 2, Issue 11, 2015.
  • Ja-Hwung Su, Wei-Yi Chang and Vincent S. Tseng. "Personalized Music Recommendation by Mining Social Media Tags". 17th International Conference in Knowledge Based and Intelligent Information and Engineering Systems - KES2013, Elsevier, 2009.
  • P. Symeonidis, M. Ruxanda, A. Nanopoulos, Y. Manolopoulos, Ternary semantic analysis of social tags for personalizedmusic recommendation, in: Proceedings of 9th International Conference on Music Information Retrieval, Philadelphia, USA, 2008, pp. 219–224.
  • Qi Q. , Chen Z. , Liu J. , Hui C. and Wu Q. "Using inferred tag ratings to improve user-based collaborative filtering". Proc. of the 27th Annual ACM Symposium on Applied Computin, pp. 2008-2013, 2012.
  • Jawaheer G. , Szomszor M. , and Kostkova P. "Comparison of implicit and explicit feedback from an online music recommendation service". ACM Recommender Systems Conference 2010, Barcelona, 2010.
  • Jawaheer G. , Szomszor M. , and Kostkova P. "Characterisation of explicit feedback in an online music recommendation service". ACM Recommender Systems Conference 2010, Barcelona, 2010.