抄録
We propose a music recommendation system based on the Kansei retrieval agent (KaRA) model using fuzzy inference to present music data that a user satisfies to obtain user preference rules for music. The KaRA model learns the user-preference using user evaluation information based on the fuzzy inference parameters of each agent that is optimized by a genetic algorithm, and it searches user-preference objects from a database. A previous study has proposed the KaRA model using fuzzy inference and applied a character coordination system as a prototype system for obtaining user-preference rules. However, we did not apply the KaRA model for music retrieval. In this study, we used the KaRA model with fuzzy reasoning to retrieve user-preference music data based on the following musical score features: main tune, tempo, the number of instrumentals, and beat, among others. Further, we examined the effectiveness of the proposed system from the viewpoint of obtaining user preference rules for music. From the experimental results, the proposed system can obtain the user preference rules as fuzzy rules of the KaRA model and recommend user preferred music data.
