Abstract
The main objective of a personalized recommender system is to filter and present (recommend) to the user the most appropriate items according to his preferences. In many Case Based Recommendation systems, thk goal is achieved by using weighted similarity measures. Thus, weighting the features, i.e. describing the items to be recommended, is a key issue in such systems. In this paper, we propose a dynamic weighting scheme for a Case Based Recommendation System, which is based on statistics of data extracted from past sessios. The applications of these ideas to an interactive, Case-Based travel recommender system, called Dietorecsl, that guides European travelers for their travel decision making processes, are described.
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