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dc.contributor.authorCraw, Susan
dc.contributor.authorHorsburgh, Ben
dc.contributor.authorMassie, Stewart
dc.contributor.editorHüllermeier, Eyke
dc.contributor.editorMinor, Mirjam
dc.identifier.citationCRAW, S., HORSBURGH, B. and MASSIE, S. 2015. Music recommendation: audio neighbourhoods to discover music in the long tail. Lecture notes in computer science [online], 9343, Proceedings of the 23rd international conference on case-based reasoning (ICCBR 2015), pages 73-87. Available from:
dc.description.abstractMillions of people use online music services every day and recommender systems are essential to browse these music collections. Users are looking for high quality recommendations, but also want to discover tracks and artists that they do not already know, newly released tracks, and the more niche music found in the ‘long tail’ of on-line music. Tag-based recommenders are not effective in this ‘long tail’ because relatively few people are listening to these tracks and so tagging tends to be sparse. However, similarity neighbourhoods in audio space can provide additional tag knowledge that is useful to augment sparse tagging. A new recommender exploits the combined knowledge, from audio and tagging, using a hybrid representation that extends the track’s tag-based representation by adding semantic knowledge extracted from the tags of similar music tracks. A user evaluation and a larger experiment using user data both show that the new hybrid recommender provides better quality recommendations than using only tags, together with a higher level of discovery of unknown and niche music. This approach of augmenting the representation for items that have missing information, with corresponding information from similar items in a complementary space, offers opportunities beyond content-based music recommendation.en
dc.subjectRecommender systemsen
dc.subjectNovelty and serendipityen
dc.subjectKnowledge extractionen
dc.subjectCBR similarity assumptionen
dc.titleMusic recommendation: audio neighbourhoods to discover music in the long tail.en
dc.typeConference publicationsen
rioxxterms.typeConference Paper/Proceeding/Abstracten

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