{"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/seq2slate-re-ranking-and-slate-optimization","title":"Seq2Slate: Re-ranking and Slate Optimization with RNNs","arxiv_id":"1810.02019","date":"2018-10-04","proceeding":"ICLR 2019 5","authors":["Irwan Bello","Sayali Kulkarni","Sagar Jain","Craig Boutilier","Ed Chi","Elad Eban","Xiyang Luo","Alan Mackey","Ofer Meshi"],"abstract":"Ranking is a central task in machine learning and information retrieval. In\nthis task, it is especially important to present the user with a slate of items\nthat is appealing as a whole. This in turn requires taking into account\ninteractions between items, since intuitively, placing an item on the slate\naffects the decision of which other items should be placed alongside it. In\nthis work, we propose a sequence-to-sequence model for ranking called\nseq2slate. At each step, the model predicts the next `best' item to place on\nthe slate given the items already selected. The sequential nature of the model\nallows complex dependencies between the items to be captured directly in a\nflexible and scalable way. We show how to learn the model end-to-end from weak\nsupervision in the form of easily obtained click-through data. We further\ndemonstrate the usefulness of our approach in experiments on standard ranking\nbenchmarks as well as in a real-world recommendation system.","url_abs":"http://arxiv.org/abs/1810.02019v3","url_pdf":"http://arxiv.org/pdf/1810.02019v3.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":"seq2slate-re-ranking-and-slate-optimization","repo_url":"https://github.com/facebookresearch/Horizon","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"seq2slate-re-ranking-and-slate-optimization","repo_url":"https://github.com/facebookresearch/ReAgent","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"information-retrieval","task_name":"Information Retrieval"},{"task_slug":"re-ranking","task_name":"Re-Ranking"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1810.02019","atlas_url":"https://app.syntology.ai/?focus=1810.02019","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}