Bias and debias in recommender system: A survey and future directions
While recent years have witnessed a rapid growth of research papers on recommender
system (RS), most of the papers focus on inventing machine learning models to better fit …
system (RS), most of the papers focus on inventing machine learning models to better fit …
Unbiased learning-to-rank with biased feedback
Implicit feedback (eg, clicks, dwell times, etc.) is an abundant source of data in human-
interactive systems. While implicit feedback has many advantages (eg, it is inexpensive to …
interactive systems. While implicit feedback has many advantages (eg, it is inexpensive to …
Unbiased learning to rank with unbiased propensity estimation
Learning to rank with biased click data is a well-known challenge. A variety of methods has
been explored to debias click data for learning to rank such as click models, result …
been explored to debias click data for learning to rank such as click models, result …
Unbiased Learning to Rank: On Recent Advances and Practical Applications
Since its inception, the field of unbiased learning to rank (ULTR) has remained very active
and has seen several impactful advancements in recent years. This tutorial provides both an …
and has seen several impactful advancements in recent years. This tutorial provides both an …
Correcting for selection bias in learning-to-rank systems
Click data collected by modern recommendation systems are an important source of
observational data that can be utilized to train learning-to-rank (LTR) systems. However …
observational data that can be utilized to train learning-to-rank (LTR) systems. However …
A survey of query auto completion in information retrieval
F Cai, M De Rijke - Foundations and Trends® in Information …, 2016 - nowpublishers.com
In information retrieval, query auto completion (QAC), also known as typeahead [Xiao et al.,
2013, Cai et al., 2014b] and auto-complete suggestion [Jain and Mishne, 2010], refers to the …
2013, Cai et al., 2014b] and auto-complete suggestion [Jain and Mishne, 2010], refers to the …
Unbiased learning to rank: online or offline?
How to obtain an unbiased ranking model by learning to rank with biased user feedback is
an important research question for IR. Existing work on unbiased learning to rank (ULTR) …
an important research question for IR. Existing work on unbiased learning to rank (ULTR) …
Recent Advances in the Foundations and Applications of Unbiased Learning to Rank
Since its inception, the field of unbiased learning to rank (ULTR) has remained very active
and has seen several impactful advancements in recent years. This tutorial provides both an …
and has seen several impactful advancements in recent years. This tutorial provides both an …
To model or to intervene: A comparison of counterfactual and online learning to rank from user interactions
Learning to Rank (LTR) from user interactions is challenging as user feedback often
contains high levels of bias and noise. At the moment, two methodologies for dealing with …
contains high levels of bias and noise. At the moment, two methodologies for dealing with …
Unifying online and counterfactual learning to rank: A novel counterfactual estimator that effectively utilizes online interventions
H Oosterhuis, M de Rijke - Proceedings of the 14th ACM international …, 2021 - dl.acm.org
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 …
the-art methods for optimizing ranking systems based on user interactions are divided into …