Browsing by Subject "Collaborative systems"

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Open Access
A collaborative system for providing routes between locations
(2008) Uluğ, Kerem Ali
Many systems, such as in-car GPS devices and airline company web sites, provide route information between locations. Although such systems are used widely and can provide route information successfully, users of these systems cannot contribute to the data entry process. In these systems, data is entered by the administrators and these systems cannot take advantage of the route expertise of their users. In this work, we present a collaborative system, which provides routes between locations upon user queries. The data in the system is entered by the users of the system. We present a model which is containing locations, links between locations and relationships between locations (containment, neighborhood and intersection) in order to store the data. For the route finding purpose, we present a customized version of the A* search algorithm. This customized version, named A*CD (A* for Collaborative Data), uses heuristics for estimating the cost remaining to the target location while processing the nodes. A*CD can also provide alternative routes, exclude certain link types in the searches according to user preferences and handle the problems associated with multiple stop transportation lines. As the cost models, we use duration and financial cost. We also present the intuitive connections concept. Even if a route does not exist between the selected locations, the system can provide a route with missing links. The gap(s) between the disconnected locations are filled by the help of the relationships between locations. In order to evaluate the performance of the A*CD algorithm, we present automated tests. These tests show that the costs of the routes that are provided by the A*CD algorithm are close to the actual shortest routes. In order to demonstrate the intuitive connections concept, we also present manual test queries.
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 As a result, there is a great deal of material which is either inaccurate or out-of-date, making it increasingly difficult to find relevant and up-to-date content.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.