Seller-Centric product recommendation rules have been realized in various RuleML projects.

In this work, we instantiate Rule Responder to a buyer-centric virtual organization, BuyerRelations, which responds to offers from sellers.

BuyerRelations makes use of a 'Computer Games' subset of the GoodRelations/eClass vocabulary, which is a generic Semantic Web ontology about products and services on the Web.

BuyerRelations screens the offers from sellers via Rule Responder's Organizational Agent,which dispatches legitimate offer queries to the Personal Agent that assists the group of buyers with the highest potential interest. The group division of buyers follows the top-level of the 'Computer Games' taxonomy. The Personal  agents use myOffer rules -- referring to the remaining levels of the 'Computer Games' taxonomy -- to filter offers through buyers' requirements based on game quantities and attributes such as prices and discounts.


Dr. Harold Boley

Advisor :

 Dr.Michalis  Vafopoulos

Team Members:  
1. Ramanpreet Singh      
2. Marine Feer               3. Sen Wang ,                        4. Jing Jing Li                    5. Chirag Sharawat

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