Recommender systems, notably collaborative and hybrid information filtering approaches, vitally depend on neighborhood formation, i.e., selecting small subsets of most relevant peers from which to receive personal product recommendations. However, common similarity-based neighborhood forming techniques imply various drawbacks, rendering the conception of decentralized recommender systems virtually impossible. We advocate trust metrics and trust-driven neighborhood formation as an appropriate surrogate, and outline various additional benefits of harnessing trust networks for recommendation generation purposes. Moreover, we present an implementation of one suchlike trust-based recommender and perform empirical analysis to underpin its fitness when coupled with an intelligent, content-based filter.