Add to ORKGSimilarity-based recommender systems suffer from significant limitations, such as data sparseness and scalability. The goal of this research is to improve recommender systems by incorporating the social concepts of trust and reputation. By introducing a trust model we can improve the quality and accuracy of the recommended items. Three trust-based recommendation strategies are presented and evaluated against the popular MovieLens [8] dataset
The incorporation of a trust network among the users of a recommender system (RS) proves beneficial ...
In collaborative filtering recommender systems, users cannot get involved in the choice of their pee...
The success of e-commerce companies is becoming increasingly dependent on product recommender system...
Similarity-based recommender systems suffer from significant limitations, such as data sparseness an...
Recommender Systems (RS) have emerged as an important response to the so-called information overload...
Recommender Systems allow people to find the resources they need by making use of the experiences a...
Recommender systems are one of the recent inventions to deal with ever growing information overload ...
Traditional collaborative filtering (CF) based recommender systems on the basis of user similarity o...
In collaborative filtering recommender systems, users cannot get involved in the choice of their pee...
In collaborative filtering recommender systems, users cannot get involved in the choice of their pee...
In collaborative filtering recommender systems, users cannot get involved in the choice of their pee...
Recommender systems help Internet users quickly find information they may be interested in from an e...
In this paper we examine an advanced collaborative filtering method that uses similarity transitivit...
Abstract. Past evidence has shown that generic approaches to recommender systems based upon collabor...
Trust networks among users of a recommender system (RS) prove beneficial to the quality and amount o...
The incorporation of a trust network among the users of a recommender system (RS) proves beneficial ...
In collaborative filtering recommender systems, users cannot get involved in the choice of their pee...
The success of e-commerce companies is becoming increasingly dependent on product recommender system...
Similarity-based recommender systems suffer from significant limitations, such as data sparseness an...
Recommender Systems (RS) have emerged as an important response to the so-called information overload...
Recommender Systems allow people to find the resources they need by making use of the experiences a...
Recommender systems are one of the recent inventions to deal with ever growing information overload ...
Traditional collaborative filtering (CF) based recommender systems on the basis of user similarity o...
In collaborative filtering recommender systems, users cannot get involved in the choice of their pee...
In collaborative filtering recommender systems, users cannot get involved in the choice of their pee...
In collaborative filtering recommender systems, users cannot get involved in the choice of their pee...
Recommender systems help Internet users quickly find information they may be interested in from an e...
In this paper we examine an advanced collaborative filtering method that uses similarity transitivit...
Abstract. Past evidence has shown that generic approaches to recommender systems based upon collabor...
Trust networks among users of a recommender system (RS) prove beneficial to the quality and amount o...
The incorporation of a trust network among the users of a recommender system (RS) proves beneficial ...
In collaborative filtering recommender systems, users cannot get involved in the choice of their pee...
The success of e-commerce companies is becoming increasingly dependent on product recommender system...