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Towards Context-Sensitive Music Recommendations Using Multifaceted User Profiles

In this paper we present an approach extracting multifaceted user profiles that enable recommendations according to a user's different preferred music styles. We describe the artists a user has listened to by making use of metadata obtained from Semantic Web data sources. After preprocessing the data we cluster a user's preferred artists and extract for each cluster a descriptive label. These labels are then aggregated to form multifaceted user profiles representing a user's full range of preferred music styles. Our evaluation experiments show that the extracted labels are specific to the artists in the clusters and can thus be used to recommend, e.g., internet radio stations and allow for an integration into existing recommendation strategies.

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AES - Audio Engineering Society