With the rapid growth of web search in recent years the problem of modeling its users has
started to attract more and more attention of the information retrieval community. This has …

A well-known problem when learning from user clicks are inherent biases prevalent in the
data, such as position or trust bias. Click models are a common method for extracting …

Search engines need to model user satisfaction to improve their services. Since it is not
practical to request feedback on searchers' perceptions and search outcomes directly from …

Many click models have been proposed to interpret logs of natural interactions with search
engines and extract unbiased information for evaluation or learning. The experimental setup …

Learning to rank~(LTR) is the de facto standard for web search, improving upon classical
retrieval models by exploiting (in) direct relevance feedback from user judgments, interaction …

Web search engines frequently show the same documents repeatedly for different queries
within the same search session, in essence forgetting when the same documents were …

Click-through information is considered as a valuable source of users' implicit relevance
feedback. As user behavior is usually influenced by a number of factors such as position …

Users' clicks on Web search results are one of the key signals for evaluating and improving
web search quality and have been widely used as part of current state-of-the-art Learning …

Research on click models usually focuses on developing effective approaches to reduce
biases in user clicks. However, one of the major drawbacks of existing click models is the …

We propose a family of new evaluation measures, called Markov Precision (MP), which
exploits continuous-time and discrete-time Markov chains in order to inject user models into …