Prevention of building-related illnesses and improving indoor air quality has become an emerging research area not only because of the comfort of workers in an office or the quality of the perceived air, but also because it can provide financial benefits to both employees and employers through a potential reduction in prolonged sick leaves. Therefore, building facility managers attempt to achieve the most comfortable and healthy environment conditions for the office workers. However, the parameters associated with achieved comfort vary from person to person as workers` preferences, as well physiological characteristics, are heterogeneous. In the ideal case, the indoor health parameters should be personalized based on individuals` feedback. This paper presents a computational framework for personalization of environmental parameters based on limited office workers’ feedback. We propose that by using current state of the art machine learning methods it is possible to learn the preference model of individuals, by employing both the limited feedback and the relevant literature on health-related symptoms. The framework is explained and discussed in a potential example scenario. Evaluation based on real data is left as a future work.