Adjusting the settings of hearing aids in a clinic is challenging as the measured thresholds of audibility do not reflect many aspects of cognitive perception or the resulting differences in auditory preferences across different contexts. Online personalization systems have a potential to solve this problem, yet the lack of contextual user preference data constitutes a major obstacle in designing and implementing them. To address this challenge, we propose a simulation-based framework to inform and accelerate the development process of online contextual personalization systems in the context of hearing aids. We discuss how to model hearing aid users and context allowing partial observability, and propose how to generate plausible preference models using Gaussian Processes incorporating assumptions about the environment in a controlled way. Finally, on a simple example we demonstrate how an uncertainty-driven agent can efficiently learn from noisy user responses within the proposed framework. We believe that such simulated environments are vital for successful development of complex context-aware online recommender systems.