Trust in recommender systems

This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid…

Trust networks among users of a recommender system (RS) prove beneficial to the quality and amount of the recommendations. Since trust is often a gradual phenomenon, fuzzy relations are the pre-eminent tools for modeling such networks. However, as…

Recommender systems have been widely used in helping people deal with information overload. In addition to traditional popular collaborative filtering recommender technology, recent research has shown that incorporating trust and reputation models…

In this paper we present novel techniques for modeling trust relationships that can be used in recommender systems. Such environments exist with the voluntary collaboration of the community members who have as a common purpose the provision of…

Recommender systems (RS) have been used for suggesting items (movies, books, songs, etc.) that users might like. RSs compute a user similarity between users and use it as a weight for the users&8217; ratings. However they have many weaknesses, such…

As the Web has shifted to an interactive environment where vast amounts of content is created by users, the question of whom to trust and what information to trust has become both more important and more difficult to answer. At the same time, social…

Recommender Systems based on Collaborative Filtering suggest to users items they might like, such as movies, songs, scientific papers, or jokes. Based on the ratings Based on the ratings provided by users about items, they first find users similar…

A recommender system's ability to establish trust with users and convince them of its recommendations, such as which camera or PC to purchase, is a crucial design factor especially for e-commerce environments. This observation led us to build a…

Recommender Systems allow people to find the resources they need by making use of the experiences and opinions of their nearest neighbours. Costly annotations by experts are replaced by a distributed process where the users take the initiative.…

Recommender systems help users find the information, products, and other people they most want to find. This tutorial provides participants with a hands-on learning experience about using recommender system technologies. After completing this…