User-Specific Music recommendation Applied to Information and Computation Resource Constrained System

The applications of item recommendation are universal in our daily life, such as job advertising, e-commercial promotion, movie and music recommendation, restaurant suggesting. However, some particular challenges emerge when it comes to music recommendation when applied to information and computation resources constrained (ICRC) platforms such as in-vehicle infotainment systems. The challenges include huge amount of total users and items, invisible user profiles, and limited in-vehicle computational resources, etc. We investigated the methods of making music recommendation for ICRC platforms in this paper. Two systems are proposed and studied, both of which are based on the collaborative filter algorithm, and designed to be target user-specific recommending so as to refrain from consuming too much computational resources. The first system remains raw user-item ratings with a goal to predict ratings from the user to other songs, while the other system focuses more on the prediction of the like behavior of a user to the songs. The configurations of the two systems are investigated. To evaluate the performance of the two systems, we include Yahoo! Music User Ratings of Songs with Artist, Album, and Genre Meta Information data set and conducted experiments. The two proposed music recommendation systems are shown to have differentiable quality in recommending abilities, e.g., mean absolute error, recall, negative recall and precision, and therefore can be applied flexibly according to practical demands.