Modelling trust in semantic web applications

This paper examines some of the barriers to the adoption of car-sharing, termed carpooling in the US, and develops a framework for trusted recommendations. The framework is established on a semantic modelling approach putting forward its suitability to resolving adoption barriers while also highlighting the characteristics of trust that can be exploited. Identification is made of potential vocabularies, ontologies and public social networks which can be used as the basis for deriving direct and indirect trust values in an implementation

Towards trust-aware recommendations in social networks

Recommender systems have been strongly researched within the last decade. With the emergence and popularization of social networks a new fi eld 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 scenarios. In this thesis a simple model for recommendations on twitter is developed to apply some of the known techniques and explore how well the state of the art does in a real scenario. The thesis can serve as a basis for further social recommender system research

Applying trust metrics based on user interactions to recommendation in social networks

Recommender systems have been strongly researched within the last decade. With the arising and popularization of digital social networks a new field has been opened for social recommendations. Considering the network topology, users interactions, or estimating trust between users are some of the new strategies that recommender systems can take into account in order to adapt their techniques to these new scenarios. We introduce MarkovTrust, a way to infer trust from Twitter interactions and to compute trust between distant users. MarkovTrust is based on Markov chains, which makes it simple to be implemented and computationally efficient. We study the properties of this trust metric and study its application in a recommender system of tweets.Postprint (published version

Enabling Social Applications via Decentralized Social Data Management

An unprecedented information wealth produced by online social networks, further augmented by location/collocation data, is currently fragmented across different proprietary services. Combined, it can accurately represent the social world and enable novel socially-aware applications. We present Prometheus, a socially-aware peer-to-peer service that collects social information from multiple sources into a multigraph managed in a decentralized fashion on user-contributed nodes, and exposes it through an interface implementing non-trivial social inferences while complying with user-defined access policies. Simulations and experiments on PlanetLab with emulated application workloads show the system exhibits good end-to-end response time, low communication overhead and resilience to malicious attacks.Comment: 27 pages, single ACM column, 9 figures, accepted in Special Issue of Foundations of Social Computing, ACM Transactions on Internet Technolog