%0 Conference Paper %1 schedl2017paths %A Schedl, Markus %A Knees, Peter %A Gouyon, Fabien %B Proceedings of the Eleventh ACM Conference on Recommender Systems %C New York, NY, USA %D 2017 %E Cremonesi, Paolo %E Ricci, Francesco %E Berkovsky, Shlomo %E Tuzhilin, Alexander %I ACM %K music overview recommendation system %P 392--393 %R 10.1145/3109859.3109934 %T New Paths in Music Recommender Systems Research %U http://doi.acm.org/10.1145/3109859.3109934 %X The particularities of musical data and its multiple modalities make original contributions possible in many core RecSys topics such as content-based and hybrid recommendation, user modeling, interfaces, and context-aware and mobile recommendations. But more urgently, the current revolution in the music industry represents major opportunities and challenges for recommendation systems in general. Recommendation systems are now central to music streaming platforms, which are rapidly increasing in listenership and becoming the top source of revenue for the music industry. It is increasingly more common for a music listener to simply access music than to purchase and own it in a personal collection. In this scenario, recommendation calls no longer for a one-shot recommendation for the purpose of a track or album purchase, but for a recommendation of a listening experience, comprising a very wide range of challenges, such as sequential recommendation, or conversational and contextual recommendations. Recommendation technologies now impact all actors in the rich and complex music industry ecosystem (listeners, labels, music makers and producers, concert halls, advertisers, etc.). To highlight these developments, we focus on three use cases: automatic playlist generation, context-aware music recommendation, and recommendation in the creative process of music making. %@ 978-1-4503-4652-8

@inproceedings{schedl2017paths, abstract = {The particularities of musical data and its multiple modalities make original contributions possible in many core RecSys topics such as content-based and hybrid recommendation, user modeling, interfaces, and context-aware and mobile recommendations. But more urgently, the current revolution in the music industry represents major opportunities and challenges for recommendation systems in general. Recommendation systems are now central to music streaming platforms, which are rapidly increasing in listenership and becoming the top source of revenue for the music industry. It is increasingly more common for a music listener to simply access music than to purchase and own it in a personal collection. In this scenario, recommendation calls no longer for a one-shot recommendation for the purpose of a track or album purchase, but for a recommendation of a listening experience, comprising a very wide range of challenges, such as sequential recommendation, or conversational and contextual recommendations. Recommendation technologies now impact all actors in the rich and complex music industry ecosystem (listeners, labels, music makers and producers, concert halls, advertisers, etc.). To highlight these developments, we focus on three use cases: automatic playlist generation, context-aware music recommendation, and recommendation in the creative process of music making.}, acmid = {3109934}, added-at = {2017-09-17T23:41:40.000+0200}, address = {New York, NY, USA}, author = {Schedl, Markus and Knees, Peter and Gouyon, Fabien}, biburl = {https://www.bibsonomy.org/bibtex/2455b072eb5ba3ae96b129aadcf60a42c/nosebrain}, booktitle = {Proceedings of the Eleventh ACM Conference on Recommender Systems}, description = {New Paths in Music Recommender Systems Research}, doi = {10.1145/3109859.3109934}, editor = {Cremonesi, Paolo and Ricci, Francesco and Berkovsky, Shlomo and Tuzhilin, Alexander}, interhash = {980d4787b3e78e90c1cc14ab4c057672}, intrahash = {455b072eb5ba3ae96b129aadcf60a42c}, isbn = {978-1-4503-4652-8}, keywords = {music overview recommendation system}, location = {Como, Italy}, numpages = {2}, pages = {392--393}, publisher = {ACM}, series = {RecSys '17}, timestamp = {2017-12-06T10:38:49.000+0100}, title = {New Paths in Music Recommender Systems Research}, url = {http://doi.acm.org/10.1145/3109859.3109934}, year = 2017 }