{"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/thuir-at-wsdm-cup-2023-task-1-unbiased","title":"THUIR at WSDM Cup 2023 Task 1: Unbiased Learning to Rank","arxiv_id":"2304.12650","date":"2023-04-25","proceeding":null,"authors":["Jia Chen","Haitao Li","Weihang Su","Qingyao Ai","Yiqun Liu"],"abstract":"This paper introduces the approaches we have used to participate in the WSDM Cup 2023 Task 1: Unbiased Learning to Rank. In brief, we have attempted a combination of both traditional IR models and transformer-based cross-encoder architectures. To further enhance the ranking performance, we also considered a series of features for learning to rank. As a result, we won 2nd place on the final leaderboard.","url_abs":"https://arxiv.org/abs/2304.12650v1","url_pdf":"https://arxiv.org/pdf/2304.12650v1.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":"thuir-at-wsdm-cup-2023-task-1-unbiased","repo_url":"https://github.com/xuanyuan14/thuir_wsdm_cup","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"paddle","reach":null}],"tasks":[{"task_slug":"learning-to-rank","task_name":"Learning-To-Rank"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2304.12650","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}