Many tasks in music information retrieval, such as recommendation, and playlist generation for online radio, fall naturally into the query-by-example setting, wherein a user queries the system by providing a song, and the system responds with a list of relevant or similar song recommendations. Such applications ultimately depend on the notion of similarity between items to produce high-quality results. Current state-of-the-art systems employ collaborative filter methods to represent musical items, effectively comparing items in terms of their constituent users. While collaborative filter techniques perform well when historical data is available for each item, their reliance on historical data impedes performance on novel or unpopular items. To combat this problem, practitioners rely on content-based similarity, which naturally extends to novel items, but is typically out-performed by collaborative filter methods. In this article, we propose a method for optimizing contentbased similarity by learning from a sample of collaborative filter data. The optimized content-based similarity metric can then be applied to answer queries on novel and unpopular items, while still maintaining high recommendation accuracy. The proposed system yields accurate and efficient representations of audio content, and experimental results show significant improvements in accuracy over competing content-based recommendation techniques.
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. Genius is a popular commercial music recommender system that is based on collaborative filtering of huge amounts of user data. To understand the aspects of music similarity that collaborative filtering can capture, we compare Genius to two canonical music recommender systems: one based purely on artist similarity, the other purely on similarity of acoustic content. We evaluate this comparison with a user study of 185 subjects. Overall, Genius produces the best recommendations. We demonstrate that collaborative filtering can actually capture similarities between the acoustic content of songs. However, when evaluators can see the names of the recommended songs and artists, we find that artist similarity can account for the performance of Genius. A system that combines these musical cues could generate music recommendations that are as good as Genius, even when collaborative filtering data is unavailable. Tags are useful text-based labels that encode semantic information about music (instrumentation, genres, emotions, geographic origins). While there are a number of ways to collect and generate tags, there is generally a data sparsity problem in which very few songs and artists have been accurately annotated with a sufficiently large set of relevant tags. We explore the idea of tag propagation to help alleviate the data sparsity problem. Tag propagation, originally proposed by , involves annotating a novel artist with tags that have been frequently associated with other similar artists. In this paper, we explore four approaches for computing artists similarity based on different sources of music information (user preference data, social tags, web documents, and audio content). We compare these approaches in terms of their ability to accurately propagate three different types of tags (genres, acoustic descriptors, social tags). We find that the approach based on collaborative filtering performs best. This is somewhat surprising considering that it is the only approach that is not explicitly based on notions of semantic similarity. We also find that tag propagation based on content-based music analysis results in relatively poor performance. 