{"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/modeling-label-ambiguity-for-neural-list-wise","title":"Modeling Label Ambiguity for Neural List-Wise Learning to Rank","arxiv_id":"1707.07493","date":"2017-07-24","proceeding":null,"authors":["Rolf Jagerman","Julia Kiseleva","Maarten de Rijke"],"abstract":"List-wise learning to rank methods are considered to be the state-of-the-art.\nOne of the major problems with these methods is that the ambiguous nature of\nrelevance labels in learning to rank data is ignored. Ambiguity of relevance\nlabels refers to the phenomenon that multiple documents may be assigned the\nsame relevance label for a given query, so that no preference order should be\nlearned for those documents. In this paper we propose a novel sampling\ntechnique for computing a list-wise loss that can take into account this\nambiguity. We show the effectiveness of the proposed method by training a\n3-layer deep neural network. We compare our new loss function to two strong\nbaselines: ListNet and ListMLE. We show that our method generalizes better and\nsignificantly outperforms other methods on the validation and test sets.","url_abs":"http://arxiv.org/abs/1707.07493v1","url_pdf":"http://arxiv.org/pdf/1707.07493v1.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":"modeling-label-ambiguity-for-neural-list-wise","repo_url":"https://github.com/rjagerman/shoelace","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","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":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}