Battle of the Music Recommender Systems: User-Centered Evaluation of Collaborative Filtering, Content-Based Analysis and Hybrid Systems

This study analyzes five music recommender systems that are also internet radio systems from a user-centric perspective. Ten artists were chosen and ten songs allowed to play for each artist on each of the five systems. Using a 10 point scale on each of five attributes established by the researcher, an overall score for each system was computed and used to rank the systems. Using the rankings, an attempt was made to establish which method (collaborative filtering, content-based analysis, or a hybrid of the two) provides the best music recommendations. Although the results were somewhat inconclusive, collaborative filtering is shown to play an important role in music recommender systems