Top-k retrieval using conditional preference networks

This paper considers top-k retrieval using Conditional Preference Network (CP-Net). As a model for expressing user preferences on multiple mutually correlated attributes, CP-Net is of great interest for decision support systems. However, little work has addressed how to conduct efficient data retrieval using CP-Nets. This paper presents an approach to efficiently retrieve the most preferred data items based on a user's CP-Net. The proposed approach consists of a top-k algorithm and an indexing scheme. We conducted extensive experiments to compare our approach against a baseline top-k method - sequential scan. The results show that our approach outperform sequential scan in several circumstances.