In large-scale distributed information systems, where participants are autonomous and have special interests for some queries, query allocation is a challenge. Much work in this context has focused on distributing queries among providers in a way that maximizes overall performance (typically throughput and response time). However, preserving the participants' interests is also important. In this paper, we make two main contributions. First, we provide a model to define participants' perception of the system w.r.t. their interests and propose metrics to evaluate the quality of query allocation methods. This model facilitates the design and evaluation of new query allocation methods that take into account the participants' interests. Second, we propose a framework for query allocation called Satisfaction-based Query Load Balancing (SQLB). To be fair, SQLB dynamically trades consumers' interests for providers' interests. And it continuously adapts to changes in participants' interests and to the workload. We implemented SQLB and compared it, through experimentation, to two important baseline query allocation methods, namely CapacityBased and Mariposa-like. The results demonstrate that SQLB yields high efficiency while satisfying the participants' interests and significantly outperforms the baseline methods.