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Interactive Music Recommendation System for Adapting Personal Affection: IMRAPA

Abstract : We have so various types of entertainment, and music is one of the most popular one. In this paper, we proposed music recommendation system that interactively adapts a user’s personal affection with only a simple operation, in which both acoustic and meta features are used. The more a user uses the proposed system, the better the system adapts the user’s personal affection and recommends the suitable songs. Through the evaluational experiment, we confirmed that the proposed system could recommend songs adapting user’s personal affection even if the personal affection variated.
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Keigo Tada, Ryosuke Yamanishi, Shohei Kato. Interactive Music Recommendation System for Adapting Personal Affection: IMRAPA. 11th International Confernece on Entertainment Computing (ICEC), Sep 2012, Bremen, Germany. pp.417-420, ⟨10.1007/978-3-642-33542-6_42⟩. ⟨hal-01556186⟩



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