We present 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 favored by the user, are fresh to the user’s ear, and fit the user’s listening pattern. We use “Forgetting Curve” to assess freshness of a song and evaluate “favoredness” using user log. We analyze user’s listening pattern to estimate the level of interest of the user in the next song. Also, we treat user behavior on the song being played as feedback to adjust the recommendation strategy for the next one. We develop an application to evaluate our approach in the real world. The user logs of trial volunteers show good performance of the proposed method.
['Yajie Hu', 'Mitsunori Ogihara']
Nextone player: A music recommendation system based on user behavior