Скачать с ютуб Recent Advances in Unbiased Learning to Rank from Position-Biased Click Feedback в хорошем качестве

Recent Advances in Unbiased Learning to Rank from Position-Biased Click Feedback

This lecture was originally presented at an internal meeting at Google; to make it available to the public I have also recorded it for Youtube. Slides are available here: https://harrieo.github.io/files/slide... This lecture discusses four recent papers in the unbiased learning to rank field: 1) Policy-Aware Unbiased Learning to Rank for Top-k Rankings - https://harrieo.github.io//publicatio... 2) Addressing Trust Bias for Unbiased Learning-to-Rank - https://research.google/pubs/pub47859/ 3) When Inverse Propensity Scoring does not Work: Affine Corrections for Unbiased Learning to Rank - https://harrieo.github.io//publicatio... 4) Unifying Online and Counterfactual Learning to Rank - https://harrieo.github.io//publicatio... For a more in-dept introduction to the field check out: our lengthy WWW'20 tutorial:    • Unbiased Learning to Rank: Counterfac...   or a shorter version:    • Introduction to Counterfactual Learni...   For more research follow me on twitter:   / harrieoos   or check out my webpage: https://harrieo.github.io/ ------- Video References ------- A. Agarwal, X. Wang, C. Li, M. Bendersky, and M. Najork. Addressing trust bias for unbiased learning-to-rank. In The World Wide Web Conference, pages 4–14. ACM, 2019a. A. Agarwal, I. Zaitsev, X. Wang, C. Li, M. Najork, and T. Joachims. Estimating position bias without intrusive interventions. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pages 474–482. ACM, 2019b. Q. Ai, K. Bi, C. Luo, J. Guo, and W. B. Croft. Unbiased learning to rank with unbiased propensity estimation. In Proceedings of the 41st International ACM SIGIR conference on Research and Development in Information Retrieval, pages 385–394. ACM, 2018. O. Chapelle and Y. Chang. Yahoo! Learning to Rank Challenge Overview. Journal of Machine Learning Research, 14:1–24, 2011. N. Craswell, O. Zoeter, M. Taylor, and B. Ramsey. An experimental comparison of click position-bias models. In Proceedings of the 2008 international conference on web search and data mining, pages 87–94, 2008. T. Joachims, L. Granka, B. Pan, H. Hembrooke, and G. Gay. Accurately interpreting clickthrough data as implicit feedback. In SIGIR Forum, pages 154–161. ACM, 2005. T. Joachims, A. Swaminathan, and T. Schnabel. Unbiased learning-to-rank with biased feedback. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, pages 781–789. ACM, 2017. H. Oosterhuis and M. de Rijke. Policy-aware unbiased learning to rank for top-k rankings. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 489–498. ACM, 2020. Z. Ovaisi, R. Ahsan, Y. Zhang, K. Vasilaky, and E. Zheleva. Correcting for selection bias in learning-to-rank systems. In Proceedings of The Web Conference 2020, pages 1863–1873, 2020. A. Vardasbi, H. Oosterhuis, and M. de Rijke. When inverse propensity scoring does not work: Affine corrections for unbiased learning to rank. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2020. X. Wang, M. Bendersky, D. Metzler, and M. Najork. Learning to rank with selection bias in personal search. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, pages 115–124. ACM, 2016. X. Wang, N. Golbandi, M. Bendersky, D. Metzler, and M. Najork. Position bias estimation for unbiased learning to rank in personal search. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pages 610–618. ACM, 2018a. X. Wang, C. Li, N. Golbandi, M. Bendersky, and M. Najork. The lambdaloss framework for ranking metric optimization. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pages 1313–1322. ACM, 2018b. ------- Chapters ------- 0:00 Lecture Overview 1:30 Introduction: Counterfactual Learning to Rank 9:28 Top-k Ranking 23:29 Trust Bias 33:28 Unifying Online and Counterfactual Learning to Rank