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Simulating user interactions enables a more user-oriented evaluation ofinformation retrieval (IR) systems. While user simulations are cost-efficientand reproducible, many approaches often lack fidelity regarding real userbehavior. Most notably, current user models neglect the user's context, whichis the primary driver of perceived relevance and the interactions with thesearch results. To this end, this work introduces the simulation ofcontext-driven query reformulations. The proposed query generation methodsbuild upon recent Large Language Model (LLM) approaches and consider the user'scontext throughout the simulation of a search session. Compared to simplecontext-free query generation approaches, these methods show bettereffectiveness and allow the simulation of more efficient IR sessions.Similarly, our evaluations consider more interaction context than currentsession-based measures and reveal interesting complementary insights inaddition to the established evaluation protocols. We conclude with directionsfor future work and provide an entirely open experimental setup.

Context-Driven Interactive Query Simulations Based on Generative Large Language Models