Abstract
The interest for providing users with suitable recommendations of songs and playlists has increased since online services for listening to music have become popular. Many methods for achieving this objective have been proposed, some of them addressed to solve well-known problems of recommender systems. However, music application domain has additional drawbacks such as the difficulty for obtaining content information and explicit ratings required by the most reliable recommender methods. In this work, a proposal for improving collaborative filtering methods is presented, whose main advantage is the use of data obtainable easily and automatically from music platforms. The method is based on a procedure for deriving ratings from user implicit behavior as well as on a new way of managing the gray-sheep problem without using content information.
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Sánchez-Moreno, D., Gil González, A.B., Muñoz Vicente, M.D., López Batista, V., Moreno-García, M.N. (2018). Recommendation of Songs in Music Streaming Services: Dealing with Sparsity and Gray Sheep Problems. In: De la Prieta, F., et al. Trends in Cyber-Physical Multi-Agent Systems. The PAAMS Collection - 15th International Conference, PAAMS 2017. PAAMS 2017. Advances in Intelligent Systems and Computing, vol 619. Springer, Cham. https://doi.org/10.1007/978-3-319-61578-3_21
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DOI: https://doi.org/10.1007/978-3-319-61578-3_21
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