With the emergence of various information access systems exhibiting increasing complexity,
there is a critical need for sound and scalable means of automatic evaluation. To address …

The logs of the use of a search engine provide sufficient data to train a better ranker.
However, it is well known that such implicit feedback reflects biases, and in particular a …

Simulation is used as a low-cost and repeatable means of experimentation. As Information
Retrieval (IR) researchers, we are no strangers to the idea of using simulation within our …

Unbiased Learning to Rank (ULTR), ie, learning to rank documents with biased user
feedback data, is a well-known challenge in information retrieval. Existing methods in …

Search engines play a crucial role in organizing and delivering information to billions of
users worldwide. However, these systems often reflect and amplify existing societal biases …

The goal of unbiased learning to rank (ULTR) is to leverage implicit user feedback for
optimizing learning-to-rank systems. Among existing solutions, automatic ULTR algorithms …

Result ranking is one of the major concerns for Web search technologies. Most existing
methodologies rank search results in descending order of relevance. To model the …

Learning-to-rank (LTR) has become a key technology in E-commerce applications. Most
existing LTR approaches follow a supervised learning paradigm with data collected from an …

Search and recommendation are crucial parts of many applications. Additionally, assistive AI
systems have become very popular with successful intelligent agent systems. Although the …

Search engines serve as one of the most important tools for accessing information online. In
modern search engines, learning to rank~(LTR) algorithms play a critical role by creating …