  Simulating user interactions enables a more user-oriented evaluation of
information retrieval (IR) systems. While user simulations are cost-efficient
and reproducible, many approaches often lack fidelity regarding real user
behavior. Most notably, current user models neglect the user's context, which
is the primary driver of perceived relevance and the interactions with the
search results. To this end, this work introduces the simulation of
context-driven query reformulations. The proposed query generation methods
build upon recent Large Language Model (LLM) approaches and consider the user's
context throughout the simulation of a search session. Compared to simple
context-free query generation approaches, these methods show better
effectiveness and allow the simulation of more efficient IR sessions.
Similarly, our evaluations consider more interaction context than current
session-based measures and reveal interesting complementary insights in
addition to the established evaluation protocols. We conclude with directions
for future work and provide an entirely open experimental setup.
