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Abstract-Many collaboration platforms are realized as service-oriented systems enabling flexible compositions of services and support of interactions. Interactions between entities in such systems do not only span software services, but also human actors. A mixed service-oriented system is therefore composed of human and software services. In open environments, interactions between people and services are highly dynamic and often influenced by the role and reputation of collaboration partners. In this paper we present an architecture for the management of trust in such mixed systems environments. In contrast to traditional solutions that typically focus on the matching of actors' skills and competencies with collaboration requirements only, we propose a trust-based 'feedback loop' enabling the inference and consideration of trust relationships based on observed interactions. This cycle, spanning interaction monitoring, trust analysis, trust-enabled collaboration planning, and trust-supported execution of activities and tasks, permits dynamic and trust-aware collaborations in service-oriented environments.

Abstract. Web-based environments typically span interactions between humans and software services. The management and automatic calculation of trust are among the key challenges of the future service-oriented Web. Trust management systems in large-scale systems, for example, social networks or service-oriented environments determine trust between actors by either collecting manual feedback ratings or by mining their interactions. However, most systems do not support bootstrapping of trust. In this paper we propose techniques and algorithms enabling the prediction of trust even when only few or no ratings have been collected or interactions captured. We introduce the concepts of mirroring and teleportation of trust facilitating the evolution of cooperation between various actors. We assume a user-centric environment, where actors express their opinions, interests and expertises by selecting and tagging resources. We take this information to construct tagging profiles, whose similarities are utilized to predict potential trust relations. Most existing similarity approaches split the three-dimensional relations between users, resources, and tags, to create and compare general tagging profiles directly. Instead, our algorithms consider (i) the understandings and interests of actors in tailored subsets of resources and (ii) the similarity of resources from a certain actor-group's point of view.