Abstract
Music data has been becoming bigger and bigger in recent years. It makes online music stores hard to provide the users with good personalized services. Therefore, a number of past studies were proposed for effectively retrieving the user preferences on music. However, they countered problems such as new user, new item and rating sparsity. To cope with these problems, in this paper, we propose a creative method that integrates information of user profiles, music genres and user ratings. In terms of solving problem of new user, the user similarities can be calculated by the profiles instead of ratings. By the user similarities, the unknown ratings can be predicted using user-based Collaborative Filtering. In terms of solving problem of rating sparsity, the unknown ratings are initialized by ratings of music genres. Even facing new music items, the rating data will not be sparse due to imputing the initialized ratings. Because the rating data is enriched, the user preference can be retrieved by item-based Collaborative Filtering. The experimental results reveal that, our proposed method performs more promising than the compared methods in terms of Root Mean Squared Error.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proeedings of International Conference on Uncertainty in Artificial Intelligence, pp. 43–52 (1998)
Cheng, R., Tang, B.: A music recommendation system based on acoustic features and user personalities. In: Cao, H., Li, J., Wang, R. (eds.) PAKDD 2016. LNCS (LNAI), vol. 9794, pp. 203–213. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-42996-0_17
Deshpande, M., Karypis, G.: Item-based top-N recommendation algorithms. ACM Trans. Inf. Syst. 22(1), 143–177 (2004)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. IEE Comput. J. 42(8), 30–37 (2009)
Nikovski, D., Kulev, V.: Induction of compact decision trees for personalized recommendation. In: Proceedings of International Conference on the ACM symposium on Applied computing, pp. 575–581 (2006)
Rahman, M.S., Rahman, M.S., Chowdhury, S.U.I., Mahmood, A., Rahman, R.M.: A personalized music recommender service based on fuzzy inference system. In: Proceedings of IEEE/ACIS 15th International Conference on Computer and Information Science (2016)
Su, J.H., Chang, W.Y., Tseng, V.S.: Personalized music recommendation by mining social media tags. In: Proceedings of the International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, pp. 291–300 (2013)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of International Conference on World Wide Web, pp. 285–295 (2001)
Shi, Y., Larson, M., Hanjalic, A.: Collaborative filtering beyond the user-item matrix: a survey of the state of the art and future challenges. ACM Comput. Surv. 47(1), 143–177 (2014)
Su, J.H., Yeh, H.H., Philip, S.Y., Tseng, V.S.: Music recommendation using content and context information mining. IEEE Intell. Syst. 25(1), 16–26 (2010)
Xu, J.A., Araki, K.: A SVM-based personal recommendation system for TV programs. In: Proceedings of International Conference on Multi-Media Modeling Conference, pp. 401–404 (2006)
Xue, H.-J., Dai, X.-Y., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17) (2017)
Wang, J., De Vries, A.P., Reinders, M.J.: Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In: Proceedings of International Conference on Special Interest Group on Information Retrieval, pp. 501–508 (2006)
Acknowledgement
This research was supported by Ministry of Science and Technology, Taiwan, R.O.C. under grant no. MOST 105-2221-E-230-011-MY2 and MOST 106-2632-S-424-001.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Su, JH., Chin, CY., Yang, HC., Tseng, V.S., Hsieh, SY. (2018). Music Recommendation Based on Information of User Profiles, Music Genres and User Ratings. In: Nguyen, N., Hoang, D., Hong, TP., Pham, H., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2018. Lecture Notes in Computer Science(), vol 10751. Springer, Cham. https://doi.org/10.1007/978-3-319-75417-8_50
Download citation
DOI: https://doi.org/10.1007/978-3-319-75417-8_50
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-75416-1
Online ISBN: 978-3-319-75417-8
eBook Packages: Computer ScienceComputer Science (R0)