Content-based music recommendation based on user preference examples
Publication from Digital
Ferdinand Fuhrmann, Dmitry Bogdanov and Martin Haro and Emilia Gomez and Perfecto Herrera
The 4th ACM Conference on Recommender Systems. Workshop on Music Recommendation and Discovery (Womrad 2010) , 1/2010
Recommending relevant and novel music to a user is one of the central applied problems in music information research. In the present work we propose three content-based approaches to this task. Starting from an explicit set of music tracks provided by the user as evidence of his/her music preferences, we infer high-level semantic descriptors, covering diff erent musical facets, such as genre, culture, moods, instruments, rhythm, and tempo. On this basis, two of the proposed approaches employ a semantic music similarity measure to generate recommendations. The third approach creates a probabilistic model of the user's preference in the semantic domain. We evaluate these approaches against two recommenders using state-of-the-art timbral features, and two contextual baselines, one exploiting simple genre categories, the other using similarity information obtained from collaborative ltering. We conduct a listening experiment to assess familiarity, liking and further listening intentions for the provided recommendations. According to the obtained results, we found our semantic approaches to outperform the low-level timbral baselines together with the genre-based recommender. Though the proposed approaches could not reach a performance comparable to the involved collaborative ltering system, they yielded acceptable results in terms of successful novel recommendations. We conclude that the proposed semantic approaches are suitable for music discovery especially in the long tail.