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 teaming 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.