Recommendation system for the selection of musical compositions

Stella Astafeva, Irina Polyakova

Abstract


This article explores the task of building a musical recommendation system. Such a system helps users to find the music content they are interested in. The paper considers the existing methods of building recommendation systems and analyzes the possibility of their application for the task of recommending musical compositions. Three basic recommendation systems are described and implemented: a system based on the popularity of compositions; a system based on the similarity of compositions by listening vectors of users; a system based on the similarity of compositions by joint auditions. Based on these basic methods, a hybrid music recommendation system has been developed and implemented. The evaluation of all received music recommendation systems was made. Among the implemented basic systems, the recommendation system based on the similarity of compositions for joint auditions turned out to be the best in terms of evaluation metrics. The proposed hybrid system showed better results than each basic one separately.


Full Text:

PDF (Russian)

References


Ricci F., Rokach L., Shapira B., Kantor P.B. Recommender Systems Handbook. Springer US, 2011. 842 p

Aggarwal C. C. Data mining. The Textbook. Springer International Publishing, 2015. 734 p

Shardanand U., Maes P. Social Information Filtering: Algorithms for Automating «Word of Mouth» // Proc. Conf. Human Factors in Computing Systems, 1995. P. 210-217

Celma O. Music Recommendation and Discovery. Springer-Verlag Berlin Heidelberg, 2010. 194 p

Adomavicius G., Tuzhilin A. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions // IEEE Transactions on Knowledge and Data Engineering, 2005. Vol. 17, Issue 6. P 734-749

Hill W., Stead L., Rosenstein M., Furnas G. Recommending and Evaluating Choices in a Virtual Community of Use // Proceeding Conference Human Factors in Computing Systems, 1995. P. 194-201

Balabanovic, M., and Shoham, Y. 1997. Fab: Content-based, collaborative recommendation. Communications of the Association of Computing Machinery 40(3):66–72

Knees P., Pohle T., Schedl M., Widmer G. Combining audio-based similarity with web-based data to accelerate automatic music playlist generation // Proceedings of the 8th ACM international workshop on Multimedia information retrieval, 2006. P. 147-154

Mango T., Sable C. A comparison of signal-based music recommendation to genre labels, collaborative filtering, musicological analysis, human recommendation, and random baseline // Proceedings of the 9th international conference on music information retrieval, 2008. P. 161-166

Goldberg D., Nichols D., Oki B. M., Terry D. Using collaborative filtering to weave an information Tapestry // Special issue on information filtering, 1992. Vol. 35, Issue 12 P. 61-70

Resnick P., Iakovou N., Sushak M., Bergstrom P., Riedl J. GroupLens: An Open Architecture for Collaborative Filtering of Netnews // Proceeding 1994 Computer Supported Cooperative Work Conference, 1994. P. 175-186

P. Melville, R. J. Mooney, and R. Nagarajan, “Content-boosted collaborative filtering for improved recommendations,” in Proceedings of the 18th National Conference on Artificial Intelligence (AAAI '02), pp. 187–192, Edmonton, Canada, 2002

Gabriel H. H., Spiliopoulou M., Nanopoulos A. Eigenvectop-based clustering using aggregated similarity matrices // Proceedings of the 2010 ACM Symposium on Applied Computing, 2010. P. 1083-1087

Schedl M., Flexer A., Urbano J. The neglected user in music information retrieval research // Journal of Intelligent Information Systems, 2013. Vol. 41, Issue 3. P. 523-539

P. Lops, M. De Gemmis, and G. Semeraro. Content-based recommender systems: State of the art and trends // In Recommender systems handbook, p. 73–105, 2011

Schafer, J. B., Konstan, J. & Riedl, J. Recommender systems in E-commerce // Proceedings of the 1st ACM conference on Electronic commerce, p. 158–166, 1999

Burke R. Hybrid recommender systems: Survey and experiments / R. Burke // User modeling and user-adapted interaction, p. 331-370, 2002.

Syed Nawaz Pasha, Dadi Ramesh, Sallauddin Mohmmad, Navya P, P. Anil Kishan and C.H Sandeep. Music Recommendation System Approaches in Machine Learning. // AIP Conference Proceedings, 2022.

B.Srikanth, V.Nagalakshmi. Songs Recommender System using Machine Learning Algorithm: SVD Algorithm. // International Journal of Innovative Science and Research Technology. Volume 5, Issue 4, 2020.

Thierry Bertin-Mahieux, Daniel P.W. Ellis, Brian Whitman, and Paul Lamere. The Million Song Dataset. // In Proceedings of the 12th International Society for Music Information Retrieval Conference (ISMIR 2011), 2011.

http://millionsongdataset.com (электронный ресурс, дата обращения 28.01.2023)

Markus Schedl, Stefan Brandl, Oleg Lesota, Emilia Parada-Cabaleiro, David Penz, Navid Rekabsaz. LFM-2b: A Dataset of Enriched Music Listening Events for Recommender Systems Research and Fairness Analysis. // CHIIR ’22, March 14–18, 2022.


Refbacks

  • There are currently no refbacks.


Abava  Кибербезопасность MoNeTec 2024

ISSN: 2307-8162