  Recommendation systems are widely used by different user service providers
specially those who have interactions with the large community of users. This
paper introduces a recommender system based on community detection. The
recommendation is provided using the local and global similarities between
users. The local information is obtained from communities, and the global ones
are based on the ratings. Here, a new fuzzy community detection using the
personalized PageRank metaphor is introduced. The fuzzy membership values of
the users to the communities are utilized to define a similarity measure. The
method is evaluated by using two well-known datasets: MovieLens and FilmTrust.
The results show that our method outperforms recent recommender systems.
