Modeling e-Procurement as Co-adaptive Matchmaking with Mutual Relevance Feedback

Abstract

Abstract. This paper proposes a new e-procurement model for a large number of buyers and sellers interacting via the Internet. The goal of e-procurement is to create a satisfactory match between buyers' demand and sellers' supply. From our real-world experience, we view e-procurement as a process of negotiation to increase the matching quality of two corresponding specifications: one for buyers' demand and another for sellers' supply. To model scalable e-procurement, we propose a co-adaptive matchmaking mechanism using mutual relevance feedback. In order to understand the nature of the mechanism, we have developed two types of software agents, called e-buyers and e-sellers, to simulate human buyers and sellers. Multiagent simulation results show that the matching quality is incrementally improved if agents adaptively change their specifications. A realistic example is also provided to discuss how to extend our simulation to real-world e-procurement infrastructure.

Sensing Semantics of RSS Feeds by Fuzzy Matchmaking

“…Information acquisition from RSS feeds requires more capability to deal with uncertainties because there is no prior agreement on how information is represented by heterogeneous publishers [4]. Logic based approaches have been widely used to support rule-based matchmaking or consistency checking by proving subsumption and (un) satisfiability [5][6].…”