Music recommendation using text analysis on song requests to radio stations |
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| Affiliation: | 1. Institute for Infocomm Research, Singapore;2. University of Hong Kong, Hong Kong;3. Centre for Computer and Information Security Research, School of Computer Science and Software Engineering, University of Wollongong, Australia;1. Research Institute of Computer Science, Technical University of Loja, San Cayetano alto, Loja, Ecuador;2. Department of Computing, Polytechnic University of Madrid, Boadilla del Monte, Madrid, Spain;1. Department of Information Technology, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia;2. Department of Electrical and Computer Engineering, Concordia University, Montreal H3G 1T7, QC, Canada;3. The Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal H3G 1T7, QC, Canada;1. School of Mathematical Science, Anhui University, Hefei, Anhui 230601, China;2. School of Business, Anhui University, Hefei, Anhui 230601, China |
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| Abstract: | Recommending appropriate music to users has always been a difficult task. In this paper, we propose a novel method in recommending music by analyzing the textual input of users. To this end, we mine a large corpus of documents from a Korean radio station’s online bulletin board. Each document, written by the listener, is composed of a song request associated with a brief, personal story. We assume that such stories are closely related with the background of the song requests and thus, our system performs text analysis to recommend songs that were requested from other similar stories. We evaluate our system using conventional metrics along with a user evaluation test. Results show that there is close correlation between document similarity and song similarity, indicating the potential of using text as a source to recommending music. |
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| Keywords: | Music recommendation Text mining Song-document association Latent Semantic Analysis Probabilistic Latent Semantic Analysis |
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