  Recommender systems, inferring users' preferences from their historical
activities and personal profiles, have been an enormous success in the last
several years. Most of the existing works are based on the similarities of
users, objects or both that derived from their purchases records in the online
shopping platforms. Such approaches, however, are facing bottlenecks when the
known information is limited. The extreme case is how to recommend products to
new users, namely the so-called cold-start problem. The rise of the online
social networks gives us a chance to break the glass ceiling. Birds of a
feather flock together. Close friends may have similar hidden pattern of
selecting products and the advices from friends are more trustworthy.
  In this paper, we integrate the individual's social relationships into
recommender systems and propose a new method, called Social Mass Diffusion
(SMD), based on a mass diffusion process in the combined network of users'
social network and user-item bipartite network. The results show that the SMD
algorithm can achieve higher recommendation accuracy than the Mass Diffusion
(MD) purely on the bipartite network. Especially, the improvement is striking
for small degree users. Moreover, SMD provides a good solution to the
cold-start problem. The recommendation accuracy for new users significantly
higher than that of the conventional popularity-based algorithm. These results
may shed some light on the new designs of better personalized recommender
systems and information services.
