Modeling preferences online

The search for an online product that matches e-shoppers’ needs and preferences can be frustrating andtime-consuming. Browsing large lists arranged in tree-like structures demands focused attention from eshoppers.Keyword search often results in either too many useless items (low precision) or few or noneuseful ones (low recall). This can cause potential buyers to seek another seller or choose to go in person toa store. This paper introduces the SPOT (Stated Preference Ontology Targeted) methodology to model eshoppers’decision-making processes and use them to refine a search and show products and services thatmeet their preferences. SPOT combines probabilistic theory on discrete choices, the theory of statedpreferences, and knowledge modeling (i.e. ontologies). The probabilistic theory on discrete choices coupledwith e-shoppers’ stated preferences data allow us to unveil parameters e-shoppers would employ to reach adecision of choice related to a given product or service. Those parameters are used to rebuild the decisionprocess and evaluate alternatives to select candidate products that are more likely to match e-shoppers’choices. We use a synthetic example to demonstrate how our approach distinguishes from currently usedmethods for e-commerce.