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 …

Click-through data has proven to be a critical resource for improving search ranking quality.
Though a large amount of click data can be easily collected by search engines, various …

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 …

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 …

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 …

Modern search systems are based on dozens or even hundreds of ranking features. The
dueling bandit gradient descent (DBGD) algorithm has been shown to effectively learn …

Learning-to-rank algorithms, which can automatically adapt ranking functions in web search,
require a large volume of training data. A traditional way of generating training examples is …

Online learning to rank is a core problem in information retrieval and machine learning.
Many provably efficient algorithms have been recently proposed for this problem in specific …

In feeds recommendation, users are able to constantly browse items generated by never-
ending feeds using mobile phones. The implicit feedback from users is an important …