Recommender systems using collaborative filtering are a well-established technique to overcome information overload in today's digital society. Currently, predominant collaborative filtering systems mostly depend on huge centralized databases to store user preferences and furthermore are only available when connected to Internet. In this paper, we consider an incremental recommender system for highly dynamic mobile environments where no central global knowledge is available and communication links are rather unreliable in comparison to static networks. We present an algorithm that aims to reach a reasonable prediction coverage and accuracy while keeping the amount of additional network overhead as small as possible, maximizing the performance of our system. For this purpose, the presented algorithm is based on a delay-tolerant broadcasting mechanism on top of a weighted cluster topology. Evaluation results show that in terms of accuracy and coverage the results of the presented algorithm converge on those obtained from a global knowledge scenario, even in the case of message loss.