Abstract
A goal for the creation and improvement of music recommendation is to retrieve users’ preferences and select the music adapting to the preferences. Although the existing researches achieved a certain degree of success and inspired future researches to get more progress, problem of the cold start recommendation and the limitation to the similar music have been pointed out. Hence we incorporate concept of serendipity using ‘renso’ alignments over Linked Data to satisfy the users’ music playing needs. We first collect music-related data from Last.fm, Yahoo! Local, Twitter and LyricWiki, and then create the ‘renso’ relation on the Music Linked Data. Our system proposes a way of finding suitable but novel music according to the users’ contexts. Finally, preliminary experiments confirm balance of accuracy and serendipity of the music recommendation.
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Acknowledgments
This work was supported by JSPS KAKENHI Grant Number 24300005C2350003-9C25730038 from Graduate School of Information Systems at University of Electro-Communications. We would like to thank Professor Shinichi Honiden in National Institute of Informatics/University of Tokyo and his group for offering a place for discussing, studying and providing instructions.
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Wang, M., Kawamura, T., Sei, Y., Nakagawa, H., Tahara, Y., Ohsuga, A. (2014). Context-Aware Music Recommendation with Serendipity Using Semantic Relations. In: Kim, W., Ding, Y., Kim, HG. (eds) Semantic Technology. JIST 2013. Lecture Notes in Computer Science(), vol 8388. Springer, Cham. https://doi.org/10.1007/978-3-319-06826-8_2
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DOI: https://doi.org/10.1007/978-3-319-06826-8_2
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