most citedQuery Resolution for Conversational Search with Limited Supervision

111 citations

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cs.IR202065 cited

Unifying Online and Counterfactual Learning to Rank

Harrie Oosterhuis, Maarten de Rijke

Optimizing ranking systems based on user interactions is a well-studied problem. State-of-the-art methods for optimizing ranking systems based on user interactions are divided into…

cs.IR202048 cited

When Inverse Propensity Scoring does not Work: Affine Corrections for Unbiased Learning to Rank

Ali Vardasbi, Harrie Oosterhuis, Maarten de Rijke

Besides position bias, which has been well-studied, trust bias is another type of bias prevalent in user interactions with rankings: users are more likely to click incorrectly w.r.…

cs.IR202021 cited

Taking the Counterfactual Online: Efficient and Unbiased Online Evaluation for Ranking

Harrie Oosterhuis, Maarten de Rijke

Counterfactual evaluation can estimate Click-Through-Rate (CTR) differences between ranking systems based on historical interaction data, while mitigating the effect of position bi…

cs.IR202012 cited

An Analysis of Mixed Initiative and Collaboration in Information-Seeking Dialogues

Svitlana Vakulenko, Evangelos Kanoulas, Maarten de Rijke

The ability to engage in mixed-initiative interaction is one of the core requirements for a conversational search system. How to achieve this is poorly understood. We propose a set…

cs.IR202035 cited

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…

cs.IR2020111 cited

Query Resolution for Conversational Search with Limited Supervision

Nikos Voskarides, Dan Li, Pengjie Ren +2

In this work we focus on multi-turn passage retrieval as a crucial component of conversational search. One of the key challenges in multi-turn passage retrieval comes from the fact…