Meuse: Recommending internet radio stations
In this paper, we describe a novel Internet radio recommendation system called MeUse. We use the Shoutcast API to collect historical data about the artists that are played on a large set of Internet radio stations. This data is used to populate an artist-station index that is similar to the term-document matrix of a traditional text-based information retrieval system. When a user wants to find stations for a given seed artist, we check the index to determine a set of stations that are either currently playing or have recently played that artist. These stations are grouped into three clusters and one representative station is selected from each cluster. This promotes diversity among the stations that are returned to the user. In addition, we provide additional information such as relevant tags (e.g., genres, emotions) and similar artists to give the user more contextual information about the recommended stations. Finally, we describe a web-based user interface that provides an interactive experience that is more like a personalized Internet radio player (e.g., Pandora) and less like a search engine for Internet radio stations (e.g., Shoutcast). A small-scale user study suggests that the majority of users enjoyed using MeUse but that providing additional contextual information may be needed to help with recommendation transparency.
Proceedings of the 14th International Society for Music Information Retrieval Conference, ISMIR 2013
Grant, Maurice; Ekanayake, Adeesha; and Turnbull, Douglas, "Meuse: Recommending internet radio stations" (2013). Faculty Articles Indexed in Scopus. 637.