{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:27:28Z","timestamp":1750220848681,"version":"3.41.0"},"reference-count":39,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2019,11,30]],"date-time":"2019-11-30T00:00:00Z","timestamp":1575072000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2019,11,30]]},"abstract":"<jats:p>\n            Someone enjoys listening to playlists while commuting. He wants a different playlist of\n            <jats:italic>n<\/jats:italic>\n            songs each day, but always starting from\n            <jats:italic>Locked Out of Heaven<\/jats:italic>\n            , a Bruno Mars song. The list should progress in smooth transitions between successive and randomly selected songs until it ends up at\n            <jats:italic>Stairway to Heaven<\/jats:italic>\n            , a Led Zeppelin song. The challenge of automatically generating random and heterogeneous playlists is to find the appropriate balance among several conflicting goals. We propose two methods for solving this problem. One is called\n            <jats:italic>ROPE<\/jats:italic>\n            , and it depends on a representation of the songs in a Euclidean space. It generates a random path through a Brownian Bridge that connects any two songs selected by the user in this music space. The second is\n            <jats:italic>STRAW<\/jats:italic>\n            , which constructs a graph representation of the music space where the nodes are songs and edges connect similar songs.\n            <jats:italic>STRAW<\/jats:italic>\n            creates a playlist by traversing the graph through a steering random walk that starts on a selected song and is directed toward a target song also selected by the user. When compared with the state-of-the-art algorithms, our algorithms are the only ones that satisfy the following quality constraints:\n            <jats:italic>heterogeneity<\/jats:italic>\n            ,\n            <jats:italic>smooth transitions<\/jats:italic>\n            ,\n            <jats:italic>novelty<\/jats:italic>\n            ,\n            <jats:italic>scalability<\/jats:italic>\n            , and\n            <jats:italic>usability<\/jats:italic>\n            . We demonstrate the usefulness of our proposed algorithms by applying them to a large collection of songs and make available a prototype.\n          <\/jats:p>","DOI":"10.1145\/3361742","type":"journal-article","created":{"date-parts":[[2019,12,16]],"date-time":"2019-12-16T13:12:30Z","timestamp":1576501950000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Random Playlists Smoothly Commuting Between Styles"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3061-6522","authenticated-orcid":false,"given":"Marcos Alves de","family":"Almeida","sequence":"first","affiliation":[{"name":"Universidade Federal de Minas Gerais, Pampulha - Belo Horizonte, Brazil"}]},{"given":"Carolina Coimbra","family":"Vieira","sequence":"additional","affiliation":[{"name":"Universidade Federal de Minas Gerais, Pampulha - Belo Horizonte, Brazil"}]},{"given":"Pedro Olmo Stancioli Vaz De","family":"Melo","sequence":"additional","affiliation":[{"name":"Universidade Federal de Minas Gerais, Pampulha - Belo Horizonte, Brazil"}]},{"given":"Renato Martins","family":"Assun\u00e7\u00e3o","sequence":"additional","affiliation":[{"name":"Universidade Federal de Minas Gerais, Pampulha - Belo Horizonte, Brazil"}]}],"member":"320","published-online":{"date-parts":[[2019,12,16]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/354384.375451"},{"volume-title":"Proceedings of the IEEE International Conference on Multimedia and Expo. 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More of an art than a science: Supporting the creation of playlists and mixes . In Proceedings of the 7th International Conference on Music Information Retrieval. 240--245 . Sally Jo Cunningham, David Bainbridge, and Annette Falconer. 2006. More of an art than a science: Supporting the creation of playlists and mixes. In Proceedings of the 7th International Conference on Music Information Retrieval. 240--245."},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-016-3836-x"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511779398"},{"key":"e_1_2_1_9_1","volume-title":"Proceedings of the International Symposium on Music Information Retrieval (ISMIR\u201908)","author":"Flexer Arthur","year":"2008","unstructured":"Arthur Flexer , Dominik Schnitzer , Martin Gasser , and Gerhard Widmer . 2008 . Playlist generation using start and end songs . In Proceedings of the International Symposium on Music Information Retrieval (ISMIR\u201908) . 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Retr-ieved on April 22, 2019 from https:\/\/www.theguardian.com\/music\/2017\/aug\/17\/they-could-destroy-the-album-how-spotify-playlists-have-changed-music-for-ever."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2010.2098858"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/WIIAT.2008.20"},{"key":"e_1_2_1_13_1","volume-title":"Proceedings of the 14th International Society for Music Information Retrieval Conference.","author":"Hauger David","year":"2013","unstructured":"David Hauger , Markus Schedl , Andrej Kosir , and Marko Tkalcic . 2013 . The million musical tweet dataset\u2014What we can learn from microblogs . In Proceedings of the 14th International Society for Music Information Retrieval Conference. David Hauger, Markus Schedl, Andrej Kosir, and Marko Tkalcic. 2013. The million musical tweet dataset\u2014What we can learn from microblogs. 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