Browsing by Subject "Trust-based systems"

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Open Access
Software design, implementation, application, and refinement of a Bayesian approach for the assessment of content and user qualities
(2011) Türk, Melihcan
The internet provides unlimited access to vast amounts of information. Technical innovations and internet coverage allow more and more people to supply contents for the web. As a result, there is a great deal of material which is either inaccurate or out-of-date, making it increasingly difficult to find relevant and up-to-date content. In order to solve this problem, recommender systems based on collaborative filtering have been introduced. These systems cluster users based on their past preferences, and suggest relevant contents according to user similarities. Trustbased recommender systems consider the trust level of users in addition to their past preferences, since some users may not be trustworthy in certain categories even though they are trustworthy in others. Content quality levels are important in order to present the most current and relevant contents to users. The study presented here is based on a model which combines the concepts of content quality and user trust. According to this model, the quality level of contents cannot be properly determined without considering the quality levels of evaluators. The model uses a Bayesian approach, which allows the simultaneous co-evaluation of evaluators and contents. The Bayesian approach also allows the calculation of the updated quality values over time. In this thesis, the model is further refined and configurable software is implemented in order to assess the qualities of users and contents on the web. Experiments were performed on a movie data set and the results showed that the Bayesian co-evaluation approach performed more effectively than a classical approach which does not consider user qualities. The approach also succeeded in classifying users according to their expertise level.
Open Access
Towards a quality service layer for Web 2.0
(Springer, 2011-12) Schaal, M.; Davenport, David; Çevik, Ali Hamdi
Despite the help of search engines and Web directories, identifying high quality content becomes increasingly difficult as the Internet gets ever more crowded with information. Prior approaches for filtering and searching content with respect to user-specific preferences do exist: Recommendation engines employ collaborative filtering to support subjective selection, (semi-)automatic page ranking algorithms utilize the hypertext link structure of the World Wide Web to assess page importance, and trust-based systems employ social network analysis to determine the most suitable Web pages. The use of implicit and explicit user feedback, however, is often either ignored or its exploitation is limited to isolated Web sites. We thus propose a quality overlay framework that enables the collection and processing of user-feedback, and the subsequent presentation of quality-enabled content for any Web-site. We present the quality overlay framework, propose an architecture for its realization, and validate our approach by scenarios and a detailed design with sample implementation. © 2011 Springer-Verlag.