Application of Trace-Based Subjective Logic to User Preferences Modeling

International audienceA good way to help users make decisions in an interactive application consists in suggesting choices in accordance with their preferences. This decision problem faces challenging tasks, mainly in choosing a good solution that satisfies users and reaches the defined goal. Classical decision methods take into account the goal, but not all the obtained decisions can satisfy users’ preferences. The originality of our explorative research is to associate Subjective Logic (SL) to system’s traces (historical information) in order to model the user preferences that improve the decision process. Following JØsang, SL provides a suitable framework for modeling and formally describing users’ preferences. We propose to connect data collected in past executions, called traces, to the user intuition in order to support subjective reasoning. Based on this result, we can choose a reasonable decision according to users’ preferences. A Tamagotchi system will be presented to validate our result

Combining recommender and reputation systems to produce better online advice

Although recommender systems and reputation systems have quite different theoretical and technical bases, both types of systems have the purpose of providing advice for decision making in e-commerce and online service environments. The similarity in purpose makes it natural to integrate both types of systems in order to produce better online advice, but their difference in theory and implementation makes the integration challenging. In this paper, we propose to use mappings to subjective opinions from values produced by recommender systems as well as from scores produced by reputation systems, and to combine the resulting opinions within the framework of subjective logic