Recommender systems have been successfully used in electronic commerce applications such as recommending books, movies, restaurants and airlines based on users’ past behaviour. More recently, such systems have made inroads into social media, for examples to recommend partners in online dating sites. In our work, we have developed a social behaviour based recommender system within an online comm...

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...

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...

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 ...

Most recommender systems usually combine several recommendation methods to enhance the accuracy. Collaborative filtering (CF) is a best-known personalized technique. While temporal association rule-based algorithm can discover users' latent interests with time-specific leveraging historical behavior data without domain knowledge. The concept-drifting and user interest-drifting are two key probl...

Recommender systems have been strongly researched within the last decade. With the emergence and popularization of social networks a new field has been opened for social recommendations. Introducing new concepts such as trust and considering the network topology are some of the new strategies that recommender systems have to take into account in order to adapt their techniques to these new scen...