Maria Cleci Martins, Rosina Weber



The search for an online product that matches e-shoppers’ needs and preferences can be frustrating and time-consuming. Browsing large lists arranged in tree-like structures demands focused attention from e-shoppers. Keyword search often results in either too many useless items (low precision) or few or none useful ones (low recall). This can cause potential buyers to seek another seller or choose to go in person to a store. This paper introduces the SPOT (Stated Preference Ontology Targeted) methodology to model e-shoppers’ decision-making processes and use them to refine a search and show products and services that meet their preferences. SPOT combines probabilistic theory on discrete choices, the theory of stated preferences, and knowledge modeling (i.e. ontologies). The probabilistic theory on discrete choices coupled with e-shoppers’ stated preferences data allow us to unveil parameters e-shoppers would employ to reach a decision of choice related to a given product or service. Those parameters are used to rebuild the decision process 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 used methods for e-commerce.


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Paper Citation

in Harvard Style

Cleci Martins M. and Weber R. (2005). MODELING PREFERENCES ONLINE . In Proceedings of the First International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 972-8865-20-1, pages 255-262. DOI: 10.5220/0001230702550262

in Bibtex Style

author={Maria Cleci Martins and Rosina Weber},
booktitle={Proceedings of the First International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},

in EndNote Style

JO - Proceedings of the First International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
SN - 972-8865-20-1
AU - Cleci Martins M.
AU - Weber R.
PY - 2005
SP - 255
EP - 262
DO - 10.5220/0001230702550262