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http://dx.doi.org/10.7838/jsebs.2017.22.3.075

Experimental Study on Random Walk Music Recommendation Considering Users' Listening Preference Behaviors  

Choe, Hye-Jin (Department of Computer Science, Sookmyung Womens University)
Shim, Junho (Department of Computer Science, Sookmyung Womens University)
Publication Information
The Journal of Society for e-Business Studies / v.22, no.3, 2017 , pp. 75-85 More about this Journal
Abstract
Personalization recommendations have already proven in many areas of the e-commerce industry. For personalization recommendations, additional work such as reclassifying items is generally necessary, which requires personal information. In this study, we propose a recommendation technique that neither exploit personal information nor reclassify items. We focus on music recommendation and performed experiments with actual music listening data. Experimental analysis shows that the proposed method may result in meaningful recommendations albeit it exploits less amount of data. We analyze the appropriate number of items and present future considerations for contextual recommendation.
Keywords
Music Recommendation; Personal Recommendation; Collaborative Filtering; Markov Chain; Pattern Analysis;
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Times Cited By KSCI : 2  (Citation Analysis)
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