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An Adaptive Music Recommendation System using Content-based Analysis and Collaborative Filtering

byIISLab 2012.03.19 07:39
저자 Kunsu Kim, Jaekwang Kim, Kwan-Ho You, Jee-Hyong Lee 
학회명 International Conference on Computer Convergence Technology 
학회명 (약자) ICCCT 2011 
pp. 206-214 
학회시작일 2011-10-20 
학회종료일 2011-10-22 
비고  
This paper proposes a user adaptive music recommendation system based on playlist analysis. The system combines a content-based and collaborative filtering approach in order to create a good recommendation list. First, we collect the playlists of all users in the system and divide each playlist into several lists of similar songs, which are called music groups. Then, based on these music groups, we create recommendation lists. The similarity between two pieces of music is defined based on the sound wave data using Mel Frequency Cepstral Coefficients (MFCCs) and the Hidden Markov Model (HMM). The pieces in each user's playlist are grouped with the similarity. Based on the music groups, a recommendation list is generated by choosing music groups close to the target user's music groups. The proposed method is evaluated using the playlists of 786 users who listen to songs through a music website, and this method is compared with a collaborative filtering and a content-based analysis. In most cases, the proposed method recommends the pieces of music which are similar to the users' preferences.
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