Hybrid personalized music recommendation method based on feature increment

Abstract

Over the past few years, the recommender system has been proposed as a critical role to help users choose the preferred product from a massive amount of data. For music recommendation, most recent recommender systems make attempts to associate music with the user’s preferences primarily based on emotions in music audio. However, this kind of recommendation mechanism ignores the emotions in lyrics and comment texts and does not consider the followers of the user, which makes the predictions unreliable. To cope with this problem, in this paper, we study the user’s listening behavior to discover his or her listening intention. We make three progresses. (1) We analyze the correlation between user preferences and the emotional categories of songs. (2) We analyze the similarity of the emotional categories of songs that users and their followees listen to. (3) We build a classification model based on KMeans and adjust different features(the correlation between the emotional category of song and user preferences, and the similarity between users and their followees) to predict whether the user will listen to the song. The experiment results verify that it is effective to consider the similarity and the correlation, and the similarity takes more effects.