Incorporating Time Dynamics and Implicit Feedback into Music Recommender Systems

Recommender systems have been applied to several domains (e.g., online streaming, e-commerce, etc.) to assist decision making. Temporal information has been recognized and demonstrated as useful factor in improving the quality of recommendations. However, it is under investigated in the area of music recommendations. In this paper, we propose to integrate time dynamics and implicit feedback in the music recommender systems. More specifically, we develop a time-aware recommendation approach in which we produce simulated ratings by aggregating implicit feedback with time dynamics. The experimental results based on the last.fm music data demonstrate the effectiveness of our proposed approach.