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.
Our research aims to tackle the problems of data sparsity and cold start of traditional recommender systems. Insufficient ratings often result in poor quality of recommendations in terms of accuracy and coverage. To address these issues, we propose three different approaches from the perspective of preference modelling. Firstly, we propose to merge the ratings of trusted neighbors and thus form a new rating profile for the active users, based on which better recommendations can be generated. Secondly, we aim to make better use of user ratings and introduce a novel Bayesian similarity measure by taking into account both the direction and length of rating vectors. Thirdly, we propose a new information source called prior ratings based on virtual product experience in virtual reality environments, in order to inherently resolve the concerned problems.