Temporal Dynamics in Music Listening Behavior: A Case Study of Online Music Service

Abstract

Although temporal context may significantly affect the popularity of items and user preference over items, traditional information filtering techniques such as recommender systems have not sufficiently considered temporal factors. Modeling temporal dynamics in user behavior is not trivial, and it is challenging to study its effect in order to provide better recommendation results to users. To incorporate temporal effects into information filtering systems, we analyze a large sized real-world usage log data gathered from Bugs Music, which is one of the well-known online music service in Korea, and study temporal dynamics in users' music listening behaviors considering periodicity of time dimension and popularity change. We insist that the result of our analysis can be a useful guideline to the industry which delivers music items to users and tries to consider temporal context in their recommendations.