Similarity Based on Rating Data

This paper describes an algorithm to measure the similarity of two multimedia objects, such as songs or movies, using users’ preferences. Much of the previous work on query-by-example (QBE) or music similarity uses detailed analysis of the object’s content. This is difficult and it is often impossible to capture how consumers react to the music. We argue that a large collection of user’s preferences is more accurate, at least in comparison to our benchmark system, at finding similar songs. We describe an algorithm based the song’s rating data, and show how this approach works by measuring its performance using an objective metric based on whether the same artist performed both songs. Our similarity results are based on 1.5 million musical judgments by 380,000 users. We test our system by generating playlists using a content-based system, our rating-based system, and a random list of songs. Music listeners greatly preferred the ratings-based playlists over the content-based and random playlists.