A Music Recommendation System Based On User Behaviors And Genre Classification

This thesis presents a new approach to recommend suitable tracks from a collection of songs to the user. The goal of the system is to recommend songs that are preferred by the user, are fresh to the user's ear, and fit the user's listening pattern. ``Forgetting Curve'' is used to assess freshness of a song and the user log is used to evaluate the preference. I analyze user's listening pattern to estimate the level of interest of the user in the next song. Also, user behavior is treated on the song being played as feedback to adjust the recommendation strategy for the next one. Furthermore, this thesis proposes a method to classify songs in the Million Song Dataset according to song genre. Since songs have several data types, several sub-classifiers are trained by different types of data. These sub-classifiers are combined using both classifier authority and classification confidence for a particular instance. In the experiments, the combined classifier surpasses all of these sub-classifiers and the SVM classifier using concatenated vectors from all data types. Finally, I develop an application to evaluate our approach in the real world.