In this chapter, we consider generative information retrieval (IR) evaluation from two distinct
but interrelated perspectives. First, Large Language Models (LLMs) themselves are rapidly …

Searching naturally involves stop** points, both at a query level (how far down the ranked
list should I go?) and at a session level (how many queries should I issue?). Understanding …

Complex search tasks—such as those from the Search as Learning (SAL) domain—often
result in users develo** an information need composed of several aspects. However …

During the information search and retrieval (ISR) process, user-system interactions such as
submitting queries, examining results, and engaging with information impose some degree …

During information search, people often experience time pressure. This might be a result of
a deadline, the system's performance or some other event. In this paper, we report results of …

Most of the current models that are used to simulate users in Interactive Information Retrieval
(IIR) lack realism and agency. Such models generally make decisions in a stochastic …

Searching for information when using a computerised retrieval system is a complex and
inherently interactive process. Individuals during a search session may issue multiple …

When performing Information Seeking and Retrieval (ISR) activities, people submit queries,
examine results, assess documents and engage with the information to make decisions and …

The interplay between the response latency of web search systems and users' search
experience has only recently started to attract research attention, despite the important …

We report preliminary results of the impact of time pressure and system delays on search
behavior from a laboratory study with forty-three participants. To induce time pressure, we …