Authors:
Maria Cleci Martins
1
and
Rosina Weber
2
Affiliations:
1
College of Businees, Brazilian Lutheran University, Brazil
;
2
College of Information Science & Technology, Drexel University, United States
Keyword(s):
E-Commerce, Discrete choices, Stated preferences, Personalization, Ontologies, User modeling
Related
Ontology
Subjects/Areas/Topics:
Data Engineering
;
e-Business and e-Commerce
;
Ontologies and the Semantic Web
;
Personalized Web Sites and Services
;
Society, e-Business and e-Government
;
User Modeling
;
Web Information Systems and Technologies
;
Web Interfaces and Applications
;
Web Personalization
Abstract:
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.
(More)