Thanks to advances in mobile technology, modern mobile devices have become essential companions, assisting their users in attaining their daily tasks. It will not be long before these devices will become recommending companions, advising users about what data (e.g., restaurants) and what services e.g., podcast channels) they may enjoy in the local area at the present time Because of the very nature of the items (both data and services being suggested (i.e., location dependent and mobile with respect to the consuming user), recommendations cannot be computed on central servers and then pushed to the users. Rather, a novel decentralised mobile recommender service will have to be developed and deployed; instead of relying on global knowledge about users’ profiles, such service will have to exploit the wisdom of local communities to compute recommendations. Moreover, because of resource limitations of mobile devices, the algorithms it will employ will have to be computationally light. In this paper, we propose diffeRS, a totally decentralised mobile recommender service specifically designed for pervasive environments. diffeRS crafts a virtual view of the local community’s preferences, by exchanging users’ profiles via radio technology (e.g., Bluetooth)during periods of colocation. Profiles are stored locally and recommendations are computed using a lightweight algorithm. As our experimental evaluations demonstrate, diffeRS achieves an accuracy and coverage that are comparable