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TRUM 2014 : The 4th Trust, Reputation and User Modeling Workshop

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Link: http://trust.sce.ntu.edu.sg/trum14
 
When Jul 7, 2014 - Jul 7, 2014
Where Aalborg, Denmark
Submission Deadline Apr 1, 2014
Categories    agents
 

Call For Papers

4th Workshop on Trust, Reputation and User Modeling (TRUM'14)
will be held with the International Conference on User Modeling Adaptation and Personalization (UMAP 2014) at Aalborg, Denmark on July 7-11, 2014, pursuing the following specific objectives:

To bring researchers together from the communities of trust and reputation modeling and user modeling;
To initiate and facilitate discussions on the new trends in trust, reputation and user modeling, and to move the trends forward.
To provide a forum for cutting-age research

Join TRUM workshop Facebook page!

There are three ways in which the area of user modeling and the area of trust and reputation modeling overlap:

First, decentralized and ubiquitous user modeling has sought inspiration from research in multi-agent systems over the last 6 years, resulting in a series of workshops at the UM conference in 2005, 2007 and UMAP 2009. The current trend towards software apps using the cloud to store and process information that can be downloaded on social networks and mobile devices platforms brings new importance to the area of decentralized user modeling. Frameworks for dynamic and purpose based sharing of user mode fragments among apps needs to take into account the trust among these apps. The trust of one agent in another can be viewed as a simple user/agent model. Researchers in the area of trust and reputation mechanisms have studied for many years techniques allowing autonomous agents and peers to share, aggregate and make decisions based on these simple user models. User modeling researchers can gain useful insights from this area.

Second, the area of trust and reputation modeling has experienced rapid growth in the past 7 years. Recently, two important trends have been emerging in this area. One is the computational modeling of agents' cognition, such as subjectivity and disposition, to achieve more accurate trust and reputation modeling. Another trend is modeling of agents' trust using a stereotype approach to deal with the problem of lack of experience. Both of these trends are closely related to studies in user modeling. The evidential success of these new trends inspires and encourages researchers in the trust community to make use of the rich literature in user modeling to develop more comprehensive trust and reputation modeling approaches.

Finally, a third important way in which research in user modeling overlaps with trust is the user’s trust in the adaptive / personalized application, e.g. a recommender system. In effect it is a symmetrical area to that of user modeling: while user modeling suggests that the system models the user, here the user models the system. It relates to issues of user’s understanding of the application, and of the privacy and integrity of the user model data, both of which are actively studied in the user modeling community. Facilitating the user’s understanding and trust in the system’s functioning and the way it manages the user’s data is very important, since it determines the user’s acceptance of the application’s recommendations or persuasion, the user’s satisfaction with the application’s functionality, and ultimately, its success. Much of the work presented at the previous TRUM workshops have focused on users trust in systems. We now look at trust in a more holistic way as shown in the figure below and manifested in online social networks: (a) trust between members of the network, (b) trust between a member and the provided online service, and (c) the trust between a member and the service provider. This focus brings yet another intersection between trust research and user modeling, with respect to recommendation systems. Whereas recommendation systems typically rely on users’ profiles or preferences, new types of recommendation algorithms are being designed based on trust behavior, thus further enhancing personalization.

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