73 citations · 522 across the 47 of their papers we have counts for
50 papers
Revisiting N2DCG: An Empirically Grounded Reformulation of Carousel Recommendation Evaluation
Jingwei Kang, Santiago de Leon-Martinez, Maarten de Rijke +1
Carousel interfaces have been widely used in video and music streaming services, yet it remains unclear how to properly evaluate recommender systems in these two-dimensional layout…
An Epistemic Position-Based Click Model: From Interactions to Epistemic Distributions of Relevance and Bias
Oscar Rolando Ramirez Milian, Harrie Oosterhuis
User interactions with rankings are affected by both items' relevances and display positions. Accordingly, click probabilities are often modeled as a product of relevance and posit…
Exposure-Based Reinforcement Learning to Rank
Harrie Oosterhuis, Rolf Jagerman, Zhen Qin +1
Reinforcement learning (RL) methods for learning-to-rank (LTR) can optimize (almost) any ranking goal, e.g., from precision or discounted cumulative gain to fairness-of-exposure or…
Following the Eye-Tracking Evidence: Established Web-Search Assumptions Fail in Carousel Interfaces
Jingwei Kang, Maarten de Rijke, Harrie Oosterhuis
Carousel interfaces have been the de-facto standard for streaming media services for over a decade. Yet, there has been very little research into user behavior with such interfaces…
A Non-Parametric Choice Model That Learns How Users Choose Between Recommended Options
Thorsten Krause, Harrie Oosterhuis
Choice models predict which items users choose from presented options. In recommendation settings, they can infer user preferences while countering exposure bias. In contrast with…
Harnessing Pairwise Ranking Prompting Through Sample-Efficient Ranking Distillation
Junru Wu, Le Yan, Zhen Qin +6
While Pairwise Ranking Prompting (PRP) with Large Language Models (LLMs) is one of the most effective zero-shot document ranking methods, it has a quadratic computational complexit…
Rethinking Click Models in Light of Carousel Interfaces: Theory-Based Categorization and Design of Click Models
Jingwei Kang, Maarten de Rijke, Santiago de Leon-Martinez +1
Click models are a well-established for modeling user interactions with web interfaces. Previous work has mainly focused on traditional single-list web search settings; this includ…
Learning to Rank with Variable Result Presentation Lengths
Norman Knyazev, Harrie Oosterhuis
Learning to Rank (LTR) methods generally assume that each document in a top-K ranking is presented in an equal format. However, previous work has shown that users' perceptions of r…
RecGaze: The First Eye Tracking and User Interaction Dataset for Carousel Interfaces
Santiago de Leon-Martinez, Jingwei Kang, Robert Moro +4
Carousel interfaces are widely used in e-commerce and streaming services, but little research has been devoted to them. Previous studies of interfaces for presenting search and rec…
Adaptive Orchestration of Modular Generative Information Access Systems
Mohanna Hoveyda, Harrie Oosterhuis, Arjen P. de Vries +2
Advancements in large language models (LLMs) have driven the emergence of complex new systems to provide access to information, that we will collectively refer to as modular genera…
Optimizing Compound Retrieval Systems
Harrie Oosterhuis, Rolf Jagerman, Zhen Qin +1
Modern retrieval systems do not rely on a single ranking model to construct their rankings. Instead, they generally take a cascading approach where a sequence of ranking models are…
A Simple and Effective Reinforcement Learning Method for Text-to-Image Diffusion Fine-tuning
Shashank Gupta, Chaitanya Ahuja, Tsung-Yu Lin +4
Reinforcement learning (RL)-based fine-tuning has emerged as a powerful approach for aligning diffusion models with black-box objectives. Proximal policy optimization (PPO) is a po…
A Simpler Alternative to Variational Regularized Counterfactual Risk Minimization
Hua Chang Bakker, Shashank Gupta, Harrie Oosterhuis
Variance regularized counterfactual risk minimization (VRCRM) has been proposed as an alternative off-policy learning (OPL) method. VRCRM method uses a lower-bound on the -diver…
AQA: Adaptive Question Answering in a Society of LLMs via Contextual Multi-Armed Bandit
Mohanna Hoveyda, Arjen P. de Vries, Maarten de Rijke +2
In question answering (QA), different questions can be effectively addressed with different answering strategies. Some require a simple lookup, while others need complex, multi-ste…
Proximal Ranking Policy Optimization for Practical Safety in Counterfactual Learning to Rank
Shashank Gupta, Harrie Oosterhuis, Maarten de Rijke
Counterfactual learning to rank (CLTR) can be risky and, in various circumstances, can produce sub-optimal models that hurt performance when deployed. Safe CLTR was introduced to m…
Practical and Robust Safety Guarantees for Advanced Counterfactual Learning to Rank
Shashank Gupta, Harrie Oosterhuis, Maarten de Rijke
Counterfactual learning to rank (CLTR) can be risky and, in various circumstances, can produce sub-optimal models that hurt performance when deployed. Safe CLTR was introduced to m…
Local Feature Selection without Label or Feature Leakage for Interpretable Machine Learning Predictions
Harrie Oosterhuis, Lijun Lyu, Avishek Anand
Local feature selection in machine learning provides instance-specific explanations by focusing on the most relevant features for each prediction, enhancing the interpretability of…
Reliable Confidence Intervals for Information Retrieval Evaluation Using Generative A.I
Harrie Oosterhuis, Rolf Jagerman, Zhen Qin +2
The traditional evaluation of information retrieval (IR) systems is generally very costly as it requires manual relevance annotation from human experts. Recent advancements in gene…
Is Interpretable Machine Learning Effective at Feature Selection for Neural Learning-to-Rank?
Lijun Lyu, Nirmal Roy, Harrie Oosterhuis +1
Neural ranking models have become increasingly popular for real-world search and recommendation systems in recent years. Unlike their tree-based counterparts, neural models are muc…
Estimating the Hessian Matrix of Ranking Objectives for Stochastic Learning to Rank with Gradient Boosted Trees
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. Their probabilistic behavior enables certain r…
Optimal Baseline Corrections for Off-Policy Contextual Bandits
Shashank Gupta, Olivier Jeunen, Harrie Oosterhuis +1
The off-policy learning paradigm allows for recommender systems and general ranking applications to be framed as decision-making problems, where we aim to learn decision policies t…
A First Look at Selection Bias in Preference Elicitation for Recommendation
Shashank Gupta, Harrie Oosterhuis, Maarten de Rijke
Preference elicitation explicitly asks users what kind of recommendations they would like to receive. It is a popular technique for conversational recommender systems to deal with…
Going Beyond Popularity and Positivity Bias: Correcting for Multifactorial Bias in Recommender Systems
Jin Huang, Harrie Oosterhuis, Masoud Mansoury +2
Two typical forms of bias in user interaction data with recommender systems (RSs) are popularity bias and positivity bias, which manifest themselves as the over-representation of i…
Consolidating Ranking and Relevance Predictions of Large Language Models through Post-Processing
Le Yan, Zhen Qin, Honglei Zhuang +4
The powerful generative abilities of large language models (LLMs) show potential in generating relevance labels for search applications. Previous work has found that directly askin…