Abstract
This paper presents a set of methods for the analysis of user activity and data preparation for the music recommender by the example of “Odnoklassniki” social network. The history of actions is being analyzed in multiple dimensions in order to find a number of collaborative and temporal correlations as well as to make the overall rankings. The results of the analysis are being exported in a form of a taste graph which is then used to generate on-line music recommendations. The taste graph displays relations between different entities connected with music (users, tracks, artists, etc.) and consists of the following main parts: user preferences, track similarities, artists’ similarities, artists’ works and demography profiles.
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Bugaychenko, D., Dzuba, A.: Musical recommendations and personalization in a social network. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp. 367–370. ACM (2013)
Desrosiers, C., Karypis, G.: A comprehensive survey of neighborhood-based recommendation methods. In: Recommender Systems Handbook, pp. 107–144 (2011)
Konstas, I., Stathopoulos, V., Jose, J.M.: On social networks and collaborative recommendation. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2009, pp. 195–202. ACM, New York (2009)
Koren, Y., Bell, R.: Advances in collaborative filtering. In: Recommender Systems Handbook, pp. 145–186 (2011)
Lovász, L.: Random walks on graphs: A survey. Combinatorics, Paul erdos is eighty 2(1), 1–46 (1993)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, pp. 285–295. ACM (2001)
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Dzuba, A., Bugaychenko, D. (2014). Mining Users Playbacks History for Music Recommendations. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2014. Lecture Notes in Computer Science(), vol 8556. Springer, Cham. https://doi.org/10.1007/978-3-319-08979-9_31
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DOI: https://doi.org/10.1007/978-3-319-08979-9_31
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08978-2
Online ISBN: 978-3-319-08979-9
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