Abstract
The Conditional Preference Network (CP-net) is one of the widely used graphical models for representing and reasoning with qualitative preferences under ceteris paribus ("all else being equal") assumptions. CP-nets have been extended to Constrained CP-nets (CCP-nets) in order to consider constraints between attributes. Adding constraints will restrict agent preferences, as some of the outcomes become infeasible. Aggregating CCP-nets (representing different agents) can be very relevant for multi-agent and recommender systems. We address this task by defining the notion of similarity between CCP-nets. The similarity is computed using the Hamming distance (between the outcomes of the related pair of CCP-nets) and the number of preference statements shared by both CCP-nets. We propose an algorithm to compute the distance between a pair of CCP-nets, based on the similarity we defined. In order to evaluate the time performance of our proposed algorithm, we conduct several experiments and report the related results.