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 …

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 …

Existing unbiased learning-to-rank models use counterfactual inference, notably Inverse
Propensity Scoring (IPS), to learn a ranking function from biased click data. They handle the …

Presentation bias is one of the key challenges when learning from implicit feedback in
search engines, as it confounds the relevance signal. While it was recently shown how …

Rankings are the primary interface through which many online platforms match users to
items (eg news, products, music, video). In these two-sided markets, not only the users draw …

A well-known challenge in learning from click data is its inherent bias and most notably
position bias. Traditional click models aim to extract the‹ query, document› relevance and …

Recently a number of algorithms under the theme of'unbiased learning-to-rank'have been
proposed, which can reduce position bias, the major type of bias in click data, and train a …

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 …

Online learning to rank for information retrieval (IR) holds promise for allowing the
development of" self-learning" search engines that can automatically adjust to their users …

Clickthrough data is a particularly inexpensive and plentiful resource to obtain implicit
relevance feedback for improving and personalizing search engines. However, it is well …