Opportunistic networks enables mobile users to participate in social interactions through applications such as content distribution, flea-market, micro-blogs and round based games. To interact securely, the establishment of trust is vital in a distributed environment. Trust is required to validate an identity and avoid sybil users, select trustworthy interaction partners or to collect useful op...

Collaborative filtering recommender systems are typically unable to generate adequate recommendations for newcomers. Empirical evidence suggests that the incorporation of a trust network among the users of a recommender system can significantly help to alleviate this problem. Hence, users are highly encouraged to connect to other users to expand the trust network, but choosing whom to connect t...

Recommender systems help users sort through vast quantities of information. Sometimes, however, users do not know if they can trust the recommendations they receive. Adding a confidence metric has the potential to improve user satisfaction and alter user behavior in a recommender system. We performed an experiment to measure the effects of a confidence display as a component of an existing coll...

In customer relationship management (CRM), online recommender assumes an important role of suggesting the right product or information to the right customer automatically. Hence customers are empowered with the choices that are predicted to be preferred by the system. The underlying technique is often a collaborative filtering (CF) algorithm that harvests both information from similar products ...

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. While the collaborative approach enables the collection of a vast amount of data, a new issue arises: the quality assessment. The elicitation of trust v...