关键词: Fisher线性判别分析, 波动序列, 音乐类型基点, 社会化标签, 音乐推荐系统

Abstract: The existing music recommendation methods often use similarity or correlation to generate recommended music list, those methods don‘t consider the volatility of users  interest reflected by the historical music behavior, which influences the recommendation accuracy. To solve this problem, this paper proposes a music recommendation method based on Fisher linear discriminant analysis and volatility sequence. In the beginning, this method obtains the social tags and audio features of music to compute the projection direction which has the minimum within-class scatter and maximum between-class scatter, by using projection transformation and Fisher discriminant criterion. This projection direction is also the best direction of classification. Then it takes music type base point as center, type volatility range as radius to acquire users’ preferred music type, and based on which to generate the recommendation list. This paper presents the empirical experiments in a real data set LFM, the results show that the proposed method can achieve better P@R and coverage rate, which means it efficiently improves recommendation accuracy and quality.

Key words: Fisher linear discriminant analysis, volatility sequence, music type base point, social tags, music recommender systems