Method for contents recommendation

Abstract

A method for music recommendation is provided using collaborative filtering methods while still managing to produce novel yet relevant items and by utilizing the long-tailed distribution of listening behavior of users, in which their playlists are biased towards a few songs while the rest of the songs, those in the long tail, have relatively low play counts. Also a link analysis method is applied to users with links between them to create an increasingly fine-grained approach in calculating weights for the recommended items. Results show that the method manages to include novel recommendations that are still relevant, and shows the potential for a new way of generating novel recommendations.