Nowadays, the emerging popularity of Social Web raises new application areas for recommender systems. The aim of a social user recommendation is to suggest new friends having similar interests. In order to identify such interests, current recommender algorithms exploit social network information or the similarity of user-generated content. The rationale of this work is that users may share simi...

The advent of the Semantic Web necessitates paradigm shifts away from centralized client/server architectures towards decentralization and peer-to-peer computation, making the existence of central authorities superfluous and even impossible. At the same time, recommender systems are gaining considerable impact in e-commerce, providing people with recommendations that are personalized and tailor...

Recommender systems have been increasingly adopted in the current Web environment, to facilitate users in efficiently locating items in which they are interested. However, most studies so far have emphasized the algorithm’s performance, rather than from the user’s perspective to investigate her/his decision-making behavior in the recommender interfaces. In this paper, we have performed a user s...

’Quality & taste’ products like wine or fine cigars are one of the fastest growing product sectors in e-commerce. Online shops for these types of products require on the one side persuasive Web presentation and on the other side deep product knowledge. In that context recommender applications may help to create an enjoyable shopping experience for online users. The Advisor Suite framework is a ...

In everyday life, we rely on recommendations from others to choose from various available options. While taking recommendations, people prefer recommendations from friends as they trust them to be their well wishers. This trust is referred to as friendship trust. A trust based recommender system where opinions are taken from trust worthy acquaintances to get personalized responses is proposed i...

In modern days, to enrich e-business, the websites are personalized for each user by understanding their interests and behavior. The main challenges of online usage data are information overload and their dynamic nature. In this paper, to address these issues, a WebBluegillRecom-annealing dynamic recommender system that uses web usage mining techniques in tandem with software agents developed f...

E-learning environments are mainly based on a range of delivery and interactive services. Web-based personalized learning recommender systems can, as a kind of services in e-learning environment, provide learning recommendations to students. This research proposes a framework of a personalized learning recommender system, which aims to help students find learning materials they would need to re...

The explosion of world-wide-web has offered people a large number of online courses, e-classes and e-schools. Such e-learning applications contain a wide variety of learning materials which can confuse the choices of learner to select. Although the area of recommender systems has made a significant progress over the last several years to address this problem, the issue remained fairly unexplore...

With the growing complexity of the Web, users often find themselves overwhelmed by the mass of choices available. Shopping for DVDs, books or clothes online becomes more and more difficult, as the variety of offers increases rapidly and gets unmanageable. To facilitate users in their selection process, recommender systems provide suggestions on items, which might be interesting for the respecti...

We describe a minimalist methodology to develop usage-based recommender systems for multimedia digital libraries. A prototype recommender system based on this strategy was implemented for the Open Video Project, a digital library of videos that are freely available for download. Sequential patterns of video retrievals are extracted from the project’s web download logs and analyzed to generate a...