35 citations · 89 across the 5 of their papers we have counts for
7 papers · 1 filter
Mixture-Based Correction for Position and Trust Bias in Counterfactual Learning to Rank
Ali Vardasbi, Maarten de Rijke, Ilya Markov
In counterfactual learning to rank (CLTR) user interactions are used as a source of supervision. Since user interactions come with bias, an important focus of research in this fiel…
Cascade Model-based Propensity Estimation for Counterfactual Learning to Rank
Ali Vardasbi, Maarten de Rijke, Ilya Markov
Unbiased CLTR requires click propensities to compensate for the difference between user clicks and true relevance of search results via IPS. Current propensity estimation methods a…
Safe Exploration for Optimizing Contextual Bandits
Rolf Jagerman, Ilya Markov, Maarten de Rijke
Contextual bandit problems are a natural fit for many information retrieval tasks, such as learning to rank, text classification, recommendation, etc. However, existing learning me…
ViTOR: Learning to Rank Webpages Based on Visual Features
Bram van den Akker, Ilya Markov, Maarten de Rijke
The visual appearance of a webpage carries valuable information about its quality and can be used to improve the performance of learning to rank (LTR). We introduce the Visual lear…
Online Learning to Rank with List-level Feedback for Image Filtering
Chang Li, Artem Grotov, Ilya Markov +1
Online learning to rank (OLTR) via implicit feedback has been extensively studied for document retrieval in cases where the feedback is available at the level of individual items.…
MergeDTS: A Method for Effective Large-Scale Online Ranker Evaluation
Chang Li, Ilya Markov, Maarten de Rijke +1
Online ranker evaluation is one of the key challenges in information retrieval. While the preferences of rankers can be inferred by interleaving methods, the problem of how to effe…