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 …

Recent advances in large language models have enabled the development of viable
generative retrieval systems. Instead of a traditional document ranking, generative retrieval …

Simulated user retrieval system interactions enable studies with controlled user behavior. To
this end, the SimIIR framework offers static, rule-based methods. We present an extended …

Users often have trouble formulating their information needs into words on the first try when
searching online. This can lead to frustration, as they may have to reformulate their queries …

We study how to develop an interpretable query simulation framework that can potentially
explain the process a real user might have used to formulate a query and propose a novel …

The existing evaluation measures for information retrieval algorithms still lack awareness
about the user's cognitive aspects and their dynamics. They often consider an isolated query …

To tackle complex IR tasks, where users cannot precisely define their needs, interaction is
paramount. Both query-reformulation approaches and chatbots are limited for this type of …

When learning something new, users generally resort to search systems. By issuing queries
and examining search results, users acquire their knowledge from the content of result …

For users working on a complex search task, it is common to address different goals at
various stages of the task through query iterations. While addressing these goals, users go …

Evaluating the interactions between users and systems presents many challenges.
Simulation offers a reliable, re-usable, and repeatable methodology to explore how different …