Recommender Systems are a primary component of online service providers, formulating plenty of information produced by users’ histories (e.g., their procurements, ratings of products, activities, browsing patterns). Recommendation algorithms use this historical information and their contextual data to offer a list of likely items for each user. Traditional recommender algorithms are built on the similarity between items or users.(e.g., a user may purchase the identical items as his nearest user). In the process of reducing limitations of traditional approaches and to improve the quality of recommender systems, a reliability based community method is introduced.This method comprises of three steps: The first step identifies the trusted relations of the current user by allowing trust propagation in the trust network. In next step, the ratings of selected trusted neighborhood are used for predicting the unrated item of current user. The prediction relies only on items that belong to candidate items’ community. Finally the reliability metric is computed to assess the worth of prediction rating. Experimental results confirmed that the proposed framework attained higher accuracy matched to state-of-the-art recommender system approaches.

Recommender systems inherently dynamic in nature and exponentially grow with time, in terms of interests and behaviour patterns. Traditional recommender systems rely on similarity of users or items in static networks where the user/item neighbourhood is almost same and they generate the same recommendations since the network is constant. This paper proposes a novel architecture, called Temporal Community-based Collaborative filtering, which is an association of recommendation and the dynamic community algorithm in order to exploit the temporal changes in the community structure to enhance the existing system. Our framework also provides solutions to common inherent issues of collaborative filtering approach such as cold-start, sparsity and compared against static and traditional collaborative systems. The outcomes indicate that the proposed system yields higher values in quality standards and minimizes the drawbacks of the traditional recommender system.