1 code implementation • 1 May 2024 • Shashank Gupta, Harrie Oosterhuis, Maarten de Rijke
Despite the fact that the extreme sparsity of preference elicitation interactions make them severely more prone to selection bias than natural interactions, the effect of selection bias in preference elicitation on the resulting recommendations has not been studied yet.
1 code implementation • 29 Apr 2024 • Jin Huang, Harrie Oosterhuis, Masoud Mansoury, Herke van Hoof, Maarten de Rijke
Debiasing methods aim to mitigate the effect of selection bias on the evaluation and optimization of RSs.
no code implementations • 18 Apr 2024 • Jingwei Kang, Maarten de Rijke, Harrie Oosterhuis
Stochastic learning to rank (LTR) is a recent branch in the LTR field that concerns the optimization of probabilistic ranking models.
no code implementations • 17 Apr 2024 • Le Yan, Zhen Qin, Honglei Zhuang, Rolf Jagerman, Xuanhui Wang, Michael Bendersky, Harrie Oosterhuis
Our method takes both LLM generated relevance labels and pairwise preferences.
1 code implementation • 6 Aug 2023 • Norman Knyazev, Harrie Oosterhuis
Most Recommender Systems (RecSys) do not provide an indication of confidence in their decisions.
no code implementations • 4 May 2023 • Shashank Gupta, Philipp Hager, Jin Huang, Ali Vardasbi, Harrie Oosterhuis
This tutorial provides both an introduction to the core concepts of the field and an overview of recent advancements in its foundations along with several applications of its methods.
1 code implementation • 26 Apr 2023 • Shashank Gupta, Harrie Oosterhuis, Maarten de Rijke
For the CLTR field, our novel exposure-based risk minimization method enables practitioners to adopt CLTR methods in a safer manner that mitigates many of the risks attached to previous methods.
1 code implementation • 20 Sep 2022 • Clara Rus, Jeffrey Luppes, Harrie Oosterhuis, Gido H. Schoenmacker
We conclude that adversarial debiasing of word representations can increase real-world fairness of systems and thus may be part of the solution for creating fairness-aware recommendation systems.
1 code implementation • 25 Jun 2022 • Norman Knyazev, Harrie Oosterhuis
Optimizing recommender systems based on user interaction data is mainly seen as a problem of dealing with selection bias, where most existing work assumes that interactions from different users are independent.
no code implementations • 24 Jun 2022 • Harrie Oosterhuis
Thus, in contrast with limitations that follow from explicit assumptions, our aim is to recognize limitations that the field is currently unaware of.
1 code implementation • 10 May 2022 • Jin Huang, Harrie Oosterhuis, Bunyamin Cetinkaya, Thijs Rood, Maarten de Rijke
In response to these shortcomings, we reproduce and expand on the existing comparison of attention-based state encoders (1) in the publicly available debiased RL4Rec SOFA simulator with (2) a different RL method, (3) more state encoders, and (4) a different dataset.
1 code implementation • 22 Apr 2022 • Harrie Oosterhuis
Plackett-Luce gradient estimation enables the optimization of stochastic ranking models within feasible time constraints through sampling techniques.
1 code implementation • 31 Mar 2022 • Harrie Oosterhuis
The prevalent approach to unbiased click-based learning-to-rank (LTR) is based on counterfactual inverse-propensity-scoring (IPS) estimation.
1 code implementation • 24 Nov 2021 • Jin Huang, Harrie Oosterhuis, Maarten de Rijke
We theoretically show that in a dynamic scenario in which both the selection bias and user preferences are dynamic, existing debiasing methods are no longer unbiased.
1 code implementation • 3 May 2021 • Harrie Oosterhuis
Unlike existing approaches that are based on policy gradients, PL-Rank makes use of the specific structure of PL models and ranking metrics.
1 code implementation • 11 Feb 2021 • Harrie Oosterhuis, Maarten de Rijke
We introduce the Generalization and Specialization (GENSPEC) algorithm, a robust feature-based counterfactual LTR method that pursues per-query memorization when it is safe to do so.
no code implementations • 9 Dec 2020 • Harrie Oosterhuis
The goal of a ranking system is to help a user find the items they are looking for with the least amount of effort.
1 code implementation • 8 Dec 2020 • Harrie Oosterhuis, Maarten de Rijke
With the introduction of the intervention-aware estimator, we aim to bridge the online/counterfactual LTR division as it is shown to be highly effective in both online and counterfactual scenarios.
1 code implementation • 24 Aug 2020 • Ali Vardasbi, Harrie Oosterhuis, Maarten de Rijke
Our main contribution is a new estimator based on affine corrections: it both reweights clicks and penalizes items displayed on ranks with high trust bias.
1 code implementation • 24 Jul 2020 • Harrie Oosterhuis, Maarten de Rijke
LogOpt turns the counterfactual approach - which is indifferent to the logging policy - into an online approach, where the algorithm decides what rankings to display.
1 code implementation • 18 May 2020 • Harrie Oosterhuis, Maarten de Rijke
We prove that the policy-aware estimator is unbiased if every relevant item has a non-zero probability to appear in the top-k ranking.
1 code implementation • 27 Nov 2019 • Ana Lucic, Harrie Oosterhuis, Hinda Haned, Maarten de Rijke
Model interpretability has become an important problem in machine learning (ML) due to the increased effect that algorithmic decisions have on humans.
no code implementations • 16 Jul 2019 • Harrie Oosterhuis, Rolf Jagerman, Maarten de Rijke
Through randomization the effect of different types of bias can be removed from the learning process.
2 code implementations • 15 Jul 2019 • Rolf Jagerman, Harrie Oosterhuis, Maarten de Rijke
At the moment, two methodologies for dealing with bias prevail in the field of LTR: counterfactual methods that learn from historical data and model user behavior to deal with biases; and online methods that perform interventions to deal with bias but use no explicit user models.
1 code implementation • 22 Sep 2018 • Harrie Oosterhuis, Maarten de Rijke
Instead, its gradient is based on inferring preferences between document pairs from user clicks and can optimize any differentiable model.
1 code implementation • 7 May 2018 • Harrie Oosterhuis, Maarten de Rijke
Existing learning to rank methods cannot handle such complex ranking settings as they assume that the display order is known beforehand.
no code implementations • 7 Sep 2016 • Harrie Oosterhuis, Sujith Ravi, Michael Bendersky
Our approach effectively captures the multimodal semantics of queries and videos using state-of-the-art deep neural networks and creates a summary that is both semantically coherent and visually attractive.