{"type":"ScholarlyArticle","title":"Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness (Extended Abstract)","authors":[{"name":"Harrie Oosterhuis","slug":"harrie-oosterhuis","given":"Harrie","family":"Oosterhuis","affiliation":"Radboud University"}],"abstract":"Computing the gradient of stochastic Plackett-Luce (PL) ranking models for relevance and fairness metrics can be infeasible because it requires iterating over all possible permutations of items.\r\nIn this paper, we introduce a novel algorithm: PL-Rank, that estimates the gradient of a PL ranking model through sampling. Unlike existing approaches, PL-Rank makes use of the specific structure of PL models and ranking metrics. Our experimental analysis shows that PL-Rank has a greater sample-efficiency and is computationally less costly than existing policy gradients, resulting in faster convergence at higher performance.","doi":"10.24963/ijcai.2022/743","url":"https://ijcai.org/proceedings/2022/743","pdf":"https://ijcai.org/proceedings/2022/0743.pdf","markdown":"https://ijcai.org/proceedings/2022/743.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/743","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Sister Conferences Best Papers","track":"Sister Conferences Best Papers","number":743,"pages":{"first":5319,"last":5323},"keywords":["Artificial Intelligence: General"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38987273","length_sec":58,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985416","length_sec":669,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Abadi and others, 2016] Martín Abadi et al. Tensorflow: A system for large-scale machine learning. In 12th USENIX symposium on operating systems design and implementa- tion OSDI'16), pages 265-283, 2016.","title":"Tensorflow: A system for large-scale machine learning","authors":"Martín Abadi, Abadi","year":"2016","venue":"12th USENIX symposium on operating systems design and implementation OSDI'16)","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Biega et al., 2018] Asia J Biega, Krishna P Gummadi, and Gerhard Weikum. Equity of attention: Amortizing individ- ual fairness in rankings. In The 41st International ACM SIGIR Conference on Research & Development in Infor- mation Retrieval, pages 405-414, 2018.","title":"Equity of attention: Amortizing individual fairness in rankings","authors":"Biega","year":"2018","venue":"The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bruch et al., 2019] Sebastian Bruch, Masrour Zoghi, Michael Bendersky, and Marc Najork. Revisiting ap- proximate metric optimization in the age of deep neural networks. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 1241-1244, 2019.","title":"Revisiting approximate metric optimization in the age of deep neural networks","authors":"Sebastian Bruch, Masrour Zoghi, Michael Bendersky, Marc Najork","year":"2019","venue":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Bruch et al., 2020] Sebastian Bruch, Shuguang Han, Michael Bendersky, and Marc Najork. A stochastic treat- ment of learning to rank scoring functions. In Proceedings of the 13th International Conference on Web Search and Data Mining, pages 61-69, 2020.","title":"A stochastic treatment of learning to rank scoring functions","authors":"Sebastian Bruch, Shuguang Han, Michael Bendersky, Marc Najork","year":"2020","venue":"Proceedings of the 13th International Conference on Web Search and Data Mining","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Burges, 2010] Christopher J.C. Burges. From ranknet to lambdarank to lambdamart: An overview. Technical Re- port MSR-TR-2010-82, Microsoft, 2010.","title":"From ranknet to lambdarank to lambdamart: An overview","authors":"J C Christopher, Burges","year":"2010","venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Cao et al., 2007] Zhe Cao, Tao Qin, Tie-Yan Liu, Ming- Feng Tsai, and Hang Li. Learning to rank: from pairwise approach to listwise approach. In Proceedings of the 24th international conference on Machine learning, pages 129- 136, 2007.","title":"Learning to rank: from pairwise approach to listwise approach","authors":"Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, Hang Li","year":"2007","venue":"Proceedings of the 24th international conference on Machine learning","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Chapelle and Chang, 2011] Olivier Chapelle and Yi Chang. Yahoo! Learning to Rank Challenge Overview. Journal of Machine Learning Research, 14:1-24, 2011.","title":"Yahoo! Learning to Rank Challenge Overview","authors":"Chang ; Chapelle, Olivier Chapelle, Yi Chang","year":"2011","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Dato et al., 2016] Domenico Dato, Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Nicola Tonellotto, and Rossano Venturini. Fast ranking with additive ensembles of oblivious and non-oblivious regression trees. ACM Transactions on Information Systems (TOIS), 35(2):Article 15, 2016.","title":"Fast ranking with additive ensembles of oblivious and non-oblivious regression trees","authors":"Domenico Dato, Claudio Lucchese, Maria Franco, Salvatore Nardini, Raffaele Orlando, Nicola Perego, Rossano Tonellotto, Venturini","year":"2016","venue":"ACM Transactions on Information Systems (TOIS)","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Diaz et al., 2020] Fernando Diaz, Bhaskar Mitra, Michael D. Ekstrand, Asia J. Biega, and Ben Carterette. Evaluating Stochastic Rankings with Expected Exposure, page 275-284. Association for Computing Machinery, New York, NY, USA, 2020.","title":"Evaluating Stochastic Rankings with Expected Exposure","authors":"Fernando Diaz, Bhaskar Mitra, Michael D Ekstrand, Asia J Biega, Ben Carterette","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Fuhr, 1989] Norbert Fuhr. Optimum polynomial retrieval functions based on the probability ranking principle. ACM Transactions on Information Systems (TOIS), 7(3):183- 204, 1989.","title":"Optimum polynomial retrieval functions based on the probability ranking principle","authors":"Norbert Fuhr","year":"1989","venue":"ACM Transactions on Information Systems (TOIS)","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Gumbel, 1954] Emil Julius Gumbel. Statistical theory of extreme values and some practical applications: a series of lectures, volume 33. US Government Printing Office, 1954.","title":"Statistical theory of extreme values and some practical applications: a series of lectures","authors":"Emil Julius Gumbel, Gumbel","year":"1954","venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Harris and others, 2020] Charles R. Harris et al. Array pro- gramming with NumPy. Nature, 585(7825):357-362, 2020.","title":"Array programming with NumPy","authors":"Charles R Harris","year":"2020","venue":"Nature","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Joachims, 2002] Thorsten Joachims. Optimizing search en- gines using clickthrough data. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 133-142. ACM, 2002.","title":"Optimizing search engines using clickthrough data","authors":"Thorsten Joachims, Joachims","year":"2002","venue":"Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Liu, 2009] Tie-Yan Liu. Learning to rank for information retrieval. Foundations and Trends in Information Re- trieval, 3(3):225-331, 2009.","title":"Learning to rank for information retrieval","authors":"Tie-Yan Liu, Liu","year":"2009","venue":"Foundations and Trends in Information Retrieval","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Luce, 2012] R Duncan Luce. Individual choice behavior: A theoretical analysis. Courier Corporation, 2012.","title":"Individual choice behavior: A theoretical analysis","authors":"Luce Duncan","year":"2012","venue":null,"doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Oosterhuis and de Rijke, 2018] Harrie Oosterhuis and Maarten de Rijke. Differentiable unbiased online learning to rank. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pages 1293-1302. ACM, 2018.","title":"Differentiable unbiased online learning to rank","authors":"Oosterhuis, Harrie De Rijke, Maarten Oosterhuis, De Rijke","year":"2018","venue":"Proceedings of the 27th ACM International Conference on Information and Knowledge Management","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Oosterhuis and de Rijke, 2020] Harrie Oosterhuis and Maarten de Rijke. Taking the counterfactual online: Efficient and unbiased online evaluation for ranking. In Proceedings of the 2020 International Conference on The Theory of Information Retrieval. ACM, 2020.","title":"Taking the counterfactual online: Efficient and unbiased online evaluation for ranking","authors":"Oosterhuis, Harrie De Rijke, Maarten Oosterhuis, De Rijke","year":"2020","venue":"Proceedings of the 2020 International Conference on The Theory of Information Retrieval","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Paszke and others, 2019] Adam Paszke et al. Pytorch: An imperative style, high-performance deep learning library. In Advances in neural information processing systems, pages 8026-8037, 2019.","title":"Pytorch: An imperative style, high-performance deep learning library","authors":"Adam Paszke","year":"2019","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Plackett, 1975] Robin L Plackett. The analysis of permuta- tions. Journal of the Royal Statistical Society: Series C (Applied Statistics), 24(2):193-202, 1975.","title":"The analysis of permutations","authors":"Robin L Plackett","year":"1975","venue":"Journal of the Royal Statistical Society: Series C (Applied Statistics)","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Qin and Liu, 2013] Tao Qin and Tie-Yan Liu. Introducing letor 4.0 datasets. arXiv preprint arXiv:1306.2597, 2013.","title":"Introducing letor 4.0 datasets","authors":"Liu Qin, Tao Qin, Tie-Yan Liu","year":"2013","venue":null,"doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Singh and Joachims, 2019] Ashudeep Singh and Thorsten Joachims. Policy learning for fairness in ranking. In Ad- vances in Neural Information Processing Systems, pages 5426-5436, 2019.","title":"Ashudeep Singh and Thorsten Joachims. Policy learning for fairness in ranking","authors":"Joachims Singh","year":"2019","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Ustimenko and Prokhorenkova, 2020] Aleksei Ustimenko and Liudmila Prokhorenkova. Stochasticrank: Global optimization of scale-free discrete functions. In In- ternational Conference on Machine Learning, pages 9669-9679. PMLR, 2020.","title":"Aleksei Ustimenko and Liudmila Prokhorenkova. Stochasticrank: Global optimization of scale-free discrete functions","authors":"Prokhorenkova Ustimenko","year":"2020","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Wang et al., 2018] Xuanhui Wang, Cheng Li, Nadav Gol- bandi, Michael Bendersky, and Marc Najork. The lambda- loss framework for ranking metric optimization. In Pro- ceedings of the 27th ACM International Conference on Information and Knowledge Management, pages 1313- 1322. ACM, 2018.","title":"The lambdaloss framework for ranking metric optimization","authors":"Wang","year":"2018","venue":"Proceedings of the 27th ACM International Conference on Information and Knowledge Management","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Williams, 1992] Ronald J Williams. Simple statistical gradient-following algorithms for connectionist reinforce- ment learning. Machine learning, 8(3-4):229-256, 1992.","title":"Simple statistical gradient-following algorithms for connectionist reinforcement learning","authors":"Williams Ronald","year":"1992","venue":"Machine learning","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Xia et al., 2008] Fen Xia, Tie-Yan Liu, Jue Wang, Wen- sheng Zhang, and Hang Li. Listwise approach to learn- ing to rank: theory and algorithm. In Proceedings of the 25th international conference on Machine learning, pages 1192-1199, 2008.","title":"Listwise approach to learning to rank: theory and algorithm","authors":"Fen Xia, Tie-Yan Liu, Jue Wang, Wensheng Zhang, Hang Li","year":"2008","venue":"Proceedings of the 25th international conference on Machine learning","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}