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Toward an Understanding of User-Defined Conditional Preferences

User-defined preferences in a natural style is useful in the pervasive computing environment, but bring a great challenge to understand. People often express conditional as well as independent preferences. We propose an ontology-based quantitative model for conditional preferences that aims to enhance the inference capacity of conditional preference statements and thus reduce users’ workload. Different interpretations of the statements of our model are depicted and compared, including the inheritance property of the concept hierarchy in ontology, the connotation of sufficient and necessary conditions, and the bipolar property of preferences in human thinking. An experiment in the trip domain is conducted and shows the feasibility of our conditional preference model.