Recommender systems have evolved during the last few years into useful online tools for assisting the daily e-commerce activities. The majority of recommender systems predict user preferences relating users with similar taste. Prior research has shown that trust networks improve the performance of recommender systems, predominantly using algorithms devised by individual researchers. In this wor...

Current recommender systems using network data focus on a single network, either a trust or a document reference network. However, in a number of applications, several types of networks need to be taken into account. The paper describes an architecture for a recommender system for publications that integrates information from different networks. Relationships in the trust network are modeled in...

We rely on the information from our trustworthy acquaintances to help us take even trivial decisions in our lives. Recommender Systems use the opinions of members of a community to help individuals in that community identify the information most likely to be interesting to them or relevant to their needs. These systems use the similarity between the user and recommenders or between the items to...

In this paper, we proposed an implicit trust relationship extraction approach to alleviate the sparsity problem in recommender systems. The recommender system cannot generate relevant items when a user-item matrix is sparse. It is a serious weakness of collaborative filtering based recommender systems. In social tagging system, tagging information is useful data source for recommendation. We in...

these days, due to growing the e-commerce sites, access to information about items is easier than past. but because of huge amount of information, we need new filtering techniques to find interested information faster and more accurate. therefore recommender systems (rs) introduced for solving this problem. although several recommender approaches have proposed, collaborative filtering (cf) appr...

Recommender systems (RS) aim to predict items that users would appreciate, over a list of items. In evaluation of recommender systems, two issues can be defined: accuracy of prediction which implies the satisfaction of the user, coverage which implies the percentage of satisfied users. Collaborative filtering (CF) is the master approach in this domain, but still has some weaknesses especially a...

Recommender systems are used extensively now-a-days for various web-sites such as for providing products suggestions based on customers’ purchase history and searched product keywords. Current recommendation system approaches lack of a high degree of stability. Diversification of prediction is also important feature of recommender system. Having displayed same set of results every time may incr...

Trust-aware recommendation approaches are widely used to mitigate the cold-start problem in recommender systems by utilizing trust networks. In this paper, we point out problems of existing trust-aware as follows: (P1) exploiting sparse explicit and distrust relationships; (P2) considering a misleading assumption that user pair having trust/distrust relationship certainly has similar/dissimilar...