Mining preference relations to rank complex objects

One of the key tasks in data mining and information retrieval is to learn preference relations between objects. Approaches reported in the literature mainly aim at learning preference relations between objects represented according to the classical attribute-value representation. However, the growing interest in data mining techniques able to directly mine data represented according to more sophisticated descriptions necessary to model more complex objects, motivates the investigation of relational learning methods for learning preference relations. In this paper, we present a probabilistic relational data mining method that permits to automatically identify preference relations between complex objects. Such preference relations are then used to identify object rankings. Experiments on a real world application are reported.