{"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/sequential-skip-prediction-with-few-shot-in","title":"Sequential Skip Prediction with Few-shot in Streamed Music Contents","arxiv_id":"1901.08203","date":"2019-01-24","proceeding":null,"authors":["Sungkyun Chang","Seungjin Lee","Kyogu Lee"],"abstract":"This paper provides an outline of the algorithms submitted for the WSDM Cup 2019 Spotify Sequential Skip Prediction Challenge (team name: mimbres). In the challenge, complete information including acoustic features and user interaction logs for the first half of a listening session is provided. Our goal is to predict whether the individual tracks in the second half of the session will be skipped or not, only given acoustic features. We proposed two different kinds of algorithms that were based on metric learning and sequence learning. The experimental results showed that the sequence learning approach performed significantly better than the metric learning approach. Moreover, we conducted additional experiments to find that significant performance gain can be achieved using complete user log information.","url_abs":"https://arxiv.org/abs/1901.08203v2","url_pdf":"https://arxiv.org/pdf/1901.08203v2.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":"sequential-skip-prediction-with-few-shot-in","repo_url":"https://github.com/mimbres/SeqSkip","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"few-shot-learning","task_name":"Few-Shot Learning"},{"task_slug":"meta-learning","task_name":"Meta-Learning"},{"task_slug":"metric-learning","task_name":"Metric Learning"},{"task_slug":"sequential-skip-prediction","task_name":"Sequential skip prediction"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dilated-causal-convolution","method_name":"Dilated Causal Convolution"},{"method_slug":"gated-convolution","method_name":"Gated Convolution"},{"method_slug":"glu","method_name":"Gated Linear Unit"},{"method_slug":"snail","method_name":"SNAIL"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/sequential-skip-prediction-on-mssd","task":"Sequential skip prediction","dataset":"MSSD","model":"Teacher","rank_in_archive_order":1,"of":2,"metrics":{"mean average accuracy":"84.9"},"uses_additional_data":false},{"leaderboard":"/sota/sequential-skip-prediction-on-mssd","task":"Sequential skip prediction","dataset":"MSSD","model":"seq1HL (2-stack)","rank_in_archive_order":2,"of":2,"metrics":{"mean average accuracy":"63.7"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}