TiMers : Time - based Music Recommendation System based on Social Network Services Analysis

Due to the explosive popularity of diverse social network services such as Twitter and Last.fm, they have become a practical and crucial source of information production and sharing it for a large number of users. For instance, Twitter is one of the biggest social networking services where a massive amount of instant messages have been published every day while Last.fm is a social music discovery service that provides personalized recommendations based on the music people listen to. In this paper, we analyzed several popular social network services (SNS) website for generating the music playlist based on the recommendation factors in terms of mood, genre and time. We performed as a case study for evaluating user satisfaction in music recommendation.