Ant colony algorithms have become recently popular in solving many optimization problems because of their collaborative decentralized behavior that mimics the behavior of real ants when foraging for food. Recommender systems present an optimization problem in which they aim to accurately predict a user’s rating for an unseen item by trying to find similar users in the network. Trust-based recom...

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...

Abstract By offering consumers more proactive and individualized information services, recommender systems have proven to be a significant answer the problem of overload. And collaborative filtering approaches an important component many such systems, allowing for development high-quality recommendations by harnessing preferences communities similar users. In this study, we argue that individua...

As the amount of data provided by various software systems increases, there is a need to offer a filtered set of items personalized to user's needs. To enhance user's comfort and thus to satisfy him, we call for recommender system. Recommender systems suggest a set of items that a user might be interested in or might find them useful. Basically, accomplishing recommendation task consists of two...

 Recommender systems are a branch of retrieval systems and information matching, which through identifying the interests and requires of the user, help the users achieve the desired information or service through a massive selection of choices. In recent years, the recommender systems apply describing information in the terms of the user, such as location, time, and task, in order to produce re...

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 the age of information explosion, e-learning recommender systems (ELRSs) have emerged as the most essential tool to deliver personalized learning resources to learners. Due to enormous amount of information on the web, learner faces problem in searching right information. ELRSs deal with the problem of information overload effectively and provide recommendations by taking into consideration ...