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The proliferation of powerful, programmable mobile devices along with the availability of wide-area connectivity has created opportunities to sense and share location, motion, acoustic and visual data in a network of mobile devices. In this paper we present a mobile recommender system that exploits the individual data collected by multiple users on their mobile phones. Our aim is to exploit the potential of the computational capabilities of modern mobile devices to provide personalized and better services to the end users. The system has been developed on a MapReduce framework that simplifies the programmability and deployment of the applications on the mobile devices and implements distributed clustering over user rating data. Experimental results over a testbed of Nokia smartphones illustrate the performance and effectiveness of our approach.