Ontology-based customer preference modeling for concept generation

Customers often present certain preferences relative to the same product, such as function, shape, color, and cost. The ideas in the mind of the customer can be represented by higher level concepts. However, the actual shape, color, and cost embodied in the product can only be viewed as lower-level features. In this paper, a model of preference elicitation from customers is proposed to bridge the gap between low-level features and high-level concepts. First, the attributes of customer preferences are classified using preference taxonomies that we develop. These taxonomies are represented using unstructured documents that are directly collected from customer descriptions. Second, the documents or catalogs of design requirements, containing some textual descriptions and survey reports, are then normalized by using an ontology-based semantic representation. Some semantic rules are developed to describe the low-level features of customer preferences to build an ontological knowledge base. Third, customer preferences are mapped to domain ontologies for driving high-level concept generation. A customer preference modeling framework is developed to construct a vector space model to measure the similarity between two preference concept ontologies. Finally, an empirical study is implemented, and five different customer groups are surveyed about the cell phone preferences. The query results are analyzed to deeply understand the validity of concept generation from the customer preferences.