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...

In recent years, collaborative filtering (CF) methods are important and widely accepted techniques are available for recommender systems. One of these techniques is user based that produces useful recommendations based on the similarity by the ratings of likeminded users. However, these systems suffer from several inherent shortcomings such as data sparsity and cold start problems. With the dev...

Recommender systems generate responses and suggest items in the required domain. This paper proposes a domain independent trust based recommender system where personalized recommendations can be generated for any domain known to the recommenders. In this recommender system, there exists a web of trust which is formed on the basis of trust among agents in application domain. Here each user captu...

In everyday life, we rely on recommendations from others to choose from various available options. While taking recommendations, people prefer recommendations from friends as they trust them to be their well wishers. This trust is referred to as friendship trust. A trust based recommender system where opinions are taken from trust worthy acquaintances to get personalized responses is proposed i...

This paper proposes the design of a recommender system that uses knowledge stored in the form of ontologies. The interactions amongst the peer agents for generating recommendations are based on the trust network that exists between them. Recommendations about a product given by peer agents are in the form of Intuitionistic Fuzzy Sets specified using degree of membership, non membership and unce...

With the rapid advancement of wireless technologies and mobile devices, service recommendations have become a crucial and important research area in mobile computing. Although various recommender systems have been developed to help users to deal with information overload, few systems focus on personalized trustworthy recommendation generation for mobile users. In real life, trust plays an impor...

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...

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...

Collaborative Filtering (CF) technique has proven to be promising for implementing large scale recommender systems but its success depends mainly on locating similar neighbors. Due to data sparsity of the user–item rating matrix, the process of finding similar neighbors does not often succeed. In addition to this, it also suffers from the new user (cold start) problem as finding possible neighb...