Distributed user profiling via spectral methods

Abstract

User profiling is a useful primitive for constructing personalized services, such as content recommendation. In the present work we investigate the feasibility of user profiling in a distributed setting, with no central authority and only local information exchanges between users. Our main contributions are: (i)~We propose a spectral clustering technique, and prove its ability to recover unknown user profiles with only few measures of affinity between users. (ii)~We develop distributed algorithms which achieve an embedding of users into a low-dimensional space, based on spectral transformation. These involve simple message passing among users, and provably converge to the desired embedding.