@phdthesis{Zerhoudi2025, author = {Zerhoudi, Saber}, title = {User Simulation in Interactive Information Retrieval : methods and frameworks for simulating complex search behavior}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-18936}, school = {Universit{\"a}t Passau}, pages = {xii, 196 Seiten}, year = {2025}, abstract = {Modern information retrieval (IR) systems, including web search engines and digital libraries, face challenges in simulating realistic user search behavior. Evolving interaction patterns and the integration of AI-powered interfaces make these challenges even harder. Traditional evaluation methods struggle to capture the dynamic nature of user interactions, particularly in complex search tasks and multi-stage information-seeking processes. User simulation offers a promising solution, providing a controlled environment for experimentation and allowing customization to model specific user behaviors and task contexts. This research develops advanced techniques for user simulation in IR, creating more realistic and dynamic models than were previously possible. Key contributions include new methods for representing query reformulation, modeling how information needs change, and measuring the impact of different search environments on simulated user behavior. Specifically, this work introduces contextual Markov models, cognitive state models, and embedding space alignment techniques to accurately represent interactive search behavior. Beyond model development, new evaluation methods and metrics are proposed for assessing the quality of simulated search sessions. These include statistical comparisons of session characteristics and classification-based approaches to distinguish between simulated and real user behavior. Additionally, this work leverages emerging technologies, such as large language models (LLMs) and retrieval-augmented generation, to improve the realism of user search behavior simulation. The practical outcome of this research is a modular and extensible simulation framework. This framework incorporates advanced techniques like user type-specific Markov models, advanced query generation using LLMs, and conversational user models.}, language = {en} }