CP-Nets Structure Learning Based on mRMCR Principle

Abstract

As a graphical model, Conditional Preference Networks (CP-nets) are used to describe the qualitative conditional preferences of attributes, and the structure learning plays an important role in the research of CP-nets. Different from traditional CP-nets structure learning methods, a Maximum Relevance Minimum Common Redundancy (mRMCR) algorithm based on the information theory and feature selection is proposed and discussed detailedly. Firstly, a mutual information solution formula on the preference database is established, it regards mutual information as mutual relation between one attribute and its feasible father set, which also avoids the calculation of conditional mutual information. Secondly, in order to make our graphical model include relevant, exclude irrelevant and control the use of redundant features, a formula for calculating mRMCR is designed. The mRMCR algorithm can measure the dependent relationship effectively and determine the causal relationship between variables, and can get the structure of CP-nets. Finally, the effectiveness of the algorithm is verified on the movie recommendation datasets. The experimental results show that the proposed mRMCR algorithm can not only obtain the causal relationship between variables quickly and effectively but also extract the feasible father set of each attribute and then obtain the topological structure of CP-nets.