Past evidence has shown that generic approaches to recommender systems based upon collaborative filtering tend to poorly scale. Moreover, their fitness for scenarios supposing distributed data storage and decentralized control, like the Semantic Web, becomes largely limited for various reasons. We believe that computational trust models bear several favorable properties for social filtering, op...

Collaborative filtering (CF) technique has been widely used in recommending items of interest to users based on social relationships. The notion of trust is emerging as an important facet of relationships in social networks. In this paper, we present an improved mechanism to the standard CF techniques by incorporating trust into CF recommendation process. We derive the trust score directly from...

In this paper we propose a recommender system that suggests the best moment to end a negotiation. The recommendation is made from a trust evaluation of every agent in the negotiation based on their past negotiation experiences. For this, we introduce the Trust Aware Negotiation Dissolution algorithm.

Recommender systems help users to identify particular items that best match their tastes or preferences. When we apply the agent theory to this domain, a standard centralized recommender system becomes a distributed world of recommender agents. Therefore, due to the agent’s world, a new information filtering method appears: the opinion-based filtering method. Its main idea is to consider other ...

Much research has recently been carried out on the incorporation of trust models into recommender systems. It is generally understood that trust-based recommender systems can help to improve the accuracy of predictions. Moreover they provide greater robustness against profile injection attacks by malicious users. In this paper we analyze these contentions in the context of two trust-based algor...

Collaborative filtering (CF) recommender systems have emerged in various applications to support item recommendation, which solve the information-overload problem by suggesting items of interest to users. Recently, trust-based recommender systems have incorporated the trustworthiness of users into CF techniques to improve the quality of recommendation. They propose trust computation models to d...

Recommender systems are important tools for users to identify their preferred items and for businesses to improve their products and services. In recent years, the use of online services for selection and reservation of hotels have witnessed a booming growth. Customer’ reviews have replaced the word of mouth marketing, but searching hotels based on user priorities is more time-consuming. This s...

Recommender systems usually support B2C e-Commerce activities without to provide e-buyers with information about the reputation of both products and interlocutors. To provide B2C traders with suggestions taking into account gossips, in this paper we present REBECCA, a fully decentralized trust-based B2C recommender system that also guarantees scalability and privacy. Some experiments show the a...

Trustworthy computing has recently attracted significant interest from researchers in several fields including multi-agent systems, social network analysis, and recommender systems. As an additional dimension of information to past rating history, trust has been shown to be helpful for improving the accuracy of recommendations. Studies on the relationship between trust and rating behaviors may ...