Trust transfer is a common technique employed in trust management systems to establish relationships between parties that are strangers. It is also well known that trust is not always transferable. That is, given an existing trust relationship, it may or may not be possible to derive new trust from it. In particular, it is not known under which constraints trust is transferable. In this paper w...

While several approaches to event recommendation already exist, a comparison study including di↵erent algorithms remains absent. We have set up an online user-centric based evaluation experiment to find a recommendation algorithm that improves user satisfaction for a popular Belgian cultural events website. Both implicit and explicit feedback in the form of user interactions with the website we...

The article deals with a problem of modelling and propagating trust in trust networks (social networks, recommender systems, integrating systems). We briefly recall some of the solutions and approaches proposed by other authors and we focus on a trust model based on IFS theory (Atanassov’s intuitionistic fuzzy set theory). Then we present a draft of a method of calculating trust propagation wit...

Recommender systems in a travel guide suggest touristic sites a user may like. Typically, people are more willing to trust recommendations from people they know. We present a trust-based recommendation service for a mobile tourist guide that uses the notion of directly and indirectly trusted peers. The recommendations combine information about the peers ratings on sights, interpersonal trust an...

Information overload is a new challenge in e-commerce sites. The problem refers to the fast growing of information that lead following the information flow in real world be impossible. Recommender systems, as the most successful application of information filtering, help users to find items of their interest from huge datasets. Collaborative filtering, as the most successful technique for recom...

The current industry standard for recommender system uses variants of collaborative filtering (CF), where recipient-source relationships are determined by the extent to which the recipient and source share interests. This research attempts to improve the performance of these CF recommender systems by identifying additional measures of relationship indicators based on theories from communication...

Online communities and networked learning provide teachers with social learning opportunities to interact and collaborate with others in order to develop their personal and professional skills. In this paper, Learning Networks are presented as an open infrastructure to provide teachers with such learning opportunities. However, with the large number of learning resources produced everyday, teac...

The Web is currently characterised by user contribution. As a result, content is generated in an uncontrolled way leading to the so-called “information overload”. The role of information filtering techniques and recommender systems is to give a solution to this problem by taking into account user preferences, and/or other context of information to present the content. Furthermore, word-of-mouth...

Recommender systems are one of the recent inventions to deal with ever growing information overload. Collaborative filtering seems to be the most popular technique in recommender systems. With sufficient background information of item ratings, its performance is promising enough. But research shows that it performs very poor in a cold start situation where previous rating data is sparse. As an ...

Current mechanisms for evaluating the trustworthiness of an agent within an electronic marketplace depend either on using a history of interactions or on recommendations from other agents. In the first case, these requirements limit what an agent with no prior interaction history can do, and in the second case, they transform the problem into one of trusting the recommending agent. However, the...