Handling conditional preferences in recommender systems

Abstract

In this paper, we propose an approach to handle conditional preferences in recommender systems. A quantitative conditional preference model based on domain knowledge is introduced. The inheritance property in concept trees and bipolar property in preference statements are adopted when interpreting conditional preference rules. Group preferences are merged from personal preferences with consideration of manipulability. A graphical user interface is developed for visualization of domain knowledge, conditional preference rules, personal and group preferences.