A User-centered Music Recommendation Approach for Daily Activities

The number of songs available on the Internet has grown steadily over the last decade, with the recent growth being due mainly to streaming services. As a consequence, it is extremely di cult for users to find the appropriate music that suit their needs, in particular, while using systems that do not have any previous information about them. This is further exacerbated while selecting appropriate songs for daily activities, like shopping, running or sleeping. In this paper we describe Improvise, a personalized music recommendation solution for daily activities, whose approach associates music content (acoustic features) with activities (context). Each activity is characterized by determining intervals for each content feature, which are then used to filter out songs to be suggested to users. While the initial intervals are generic enough to provide recommendations for di↵erent activities without having previous knowledge about the user’s tastes, our approach also considers users’ feedback to personalize the recommendations for each user and activity. This is done by adapting the intervals according to the feedback from users. Preliminary evaluation shows that we are on the good path to achieve the goal of developing a solution to e↵ectively recommend songs for daily activities, and able to adjust to individual user’s tastes, increasing their satisfaction.