Music services provide listeners access to great numbers of available tracks. It is time consuming for listeners to find potential favorite ones. Music listeners increasingly want playlists to be created automatically. This study examines the relationship between background knowledge about music and track co-occurrence frequency in users’ music listening history and builds a multiple linear regression model to predict the track co-occurrence. So given a seed track, the model can find out which track is most likely to co-occur. A simple objective evaluation compares predicted track with tracks in the users’ listening history. 13 out of 15 test tracks find the highest rank predicted track in the same listening history.Master of Science in Information Scienc
