Modeling of user interest based on its interaction with a collaborative knowledge management system

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-02580-8_36Proceedings of 13th International Conference, HCI International 2009, San Diego, CA, USA, July 19-24, 2009, Part IIISKC is a prototype system for knowledge management in the Web by means of semantic information without supervision and tries to select the knowledge contained in the system by paying attention to its use. This paper explains user activity analysis in order to find out their interest for knowledge elements in the system, and the application of this interest for users classification and knowledge identification for their interest, inside and outside SKC. As a result a model for user interest based on interaction is obtained.This research has been partially financed by the Spanish Ministry of Science and Technology, through TIN2007-64718 and TIN2008-02081/TIN projects, and by the Spanish Agency for the International Cooperation (AECI) through A/7954/07 project

Intended Deception in the Virtual World

This study explores how people intend to deceive in the virtual world. Previous research has focused the intent and behavior of online deception, but has rarely looked into specific aspects of online deception including strategy, magnitude, and seriousness. We answered research questions about people’s selection of deception strategies, perceived seriousness of deception, and magnitude of deception in the virtual world via a survey study. Additionally, we examined possible influence of age and gender on deception. The findings are interesting and offer implications for designing deception detection strategies

Comparing Approaches for Weighting Applications Specific Data in Multi-Application User Interest Modeling

This thesis presents a framework known as User Interest Modeling and Personalization (UIMAP) which builds a model by identifying and aggregating an individual user's interest expressed through their interactions with different applications at different times. To do this, we have implemented a content consumer/producer architecture. For this thesis, Microsoft Word and PowerPoint are treated as content producer applications while a web browser is used as a content consumer application. We unobtrusively observe user interactions with these applications as well as the actual content consumed/prepared in them. The challenge is to understand the importance of each application towards the user's real interest. Based on user activity data in these applications, Multilayer Perceptron (MLP), Support Vector Machine (SVM) and Weighted K-Nearest Neighborhood (WKNN) techniques are compared in their ability to combine these kinds of heterogeneous interest indicators into a single model. Thus, each application is weighted differently based on its contributing indicators to predict the relevant content for the specific need of an individual. We found that textual content from content producer applications plays an equally important role as content from consumer applications. Implicit feedbacks from consumer applications also have a major role in user's interest. The results indicated that WKNN is preferred if feature weighting is the primary goal while SVM is the preferred choice if identifying relevant content is the main objective

Modeling user interest shift using a bayesian approach

We investigate the modeling of changes in user interest in information filtering systems. A new technique for tracking user interest shifts based on a Bayesian approach is developed. The interest tracker is integrated into a profile learning module of a filtering system. We present an analytical study to establish the rate of convergence for the profile learning with and without the user interest tracking component. We examine the relationship among degree of shift, cost of detection error, and time needed for detection. To study the effect of different patterns of interest shift on system performance we also conducted several filtering experiments. Generally, the findings show that the Bayesian approach is a feasible and effective technique for modeling user interest shift