{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/modelling-sequential-music-track-skips-using","title":"Modelling Sequential Music Track Skips using a Multi-RNN Approach","arxiv_id":"1903.08408","date":"2019-03-20","proceeding":null,"authors":["Christian Hansen","Casper Hansen","Stephen Alstrup","Jakob Grue Simonsen","Christina Lioma"],"abstract":"Modelling sequential music skips provides streaming companies the ability to\nbetter understand the needs of the user base, resulting in a better user\nexperience by reducing the need to manually skip certain music tracks. This\npaper describes the solution of the University of Copenhagen DIKU-IR team in\nthe 'Spotify Sequential Skip Prediction Challenge', where the task was to\npredict the skip behaviour of the second half in a music listening session\nconditioned on the first half. We model this task using a Multi-RNN approach\nconsisting of two distinct stacked recurrent neural networks, where one network\nfocuses on encoding the first half of the session and the other network focuses\non utilizing the encoding to make sequential skip predictions. The encoder\nnetwork is initialized by a learned session-wide music encoding, and both of\nthem utilize a learned track embedding. Our final model consists of a majority\nvoted ensemble of individually trained models, and ranked 2nd out of 45\nparticipating teams in the competition with a mean average accuracy of 0.641\nand an accuracy on the first skip prediction of 0.807. Our code is released at\nhttps://github.com/Varyn/WSDM-challenge-2019-spotify.","url_abs":"http://arxiv.org/abs/1903.08408v1","url_pdf":"http://arxiv.org/pdf/1903.08408v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"modelling-sequential-music-track-skips-using","repo_url":"https://github.com/Varyn/WSDM-challenge-2019-spotify","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"sequential-skip-prediction","task_name":"Sequential skip prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}