Introduction to the Special Issue: Matching Buyers and Sellers for e-Commerce
Michael P. Wellman and John Riedl, Guest Editors
International Journal of Electronic Commerce,
Volume 8, Number 4, Summer 2004, pp. 7.
The five papers in this Special Issue span a wide range of interesting topics, all related to the challenge of matching buyers and sellers for electronic commerce. All five were presented at the 2003 World-Wide Web Conference, representing the best papers submitted to the E-Commerce Track of that highly selective forum. Three of the papers in the Special Issue are about knowledge representation, and two of these are about representing high-level offerings and requirements in order to support automated matchmaking of potential sellers and buyers. Such matchmaking is one way that electronic marketplaces can reduce friction compared to their physical-world counterparts, since information about buyers and sellers can be made available nearly instantly at very low cost.
“A System for Principled Matchmaking in an Electronic Marketplace,” by Tommaso Di Noia, Eugenio Di Sciascio, Francesco M. Donini, and Marina Mongiello, begins with a description of the requirements for matchmaking in e-commerce. The paper then carefully describes the CLASSIC description logic system and how it provides a rich semantics for matchmaking while still yielding tractable performance. NeoClassic, a C++ implementation of CLASSIC, is used for experiments to compare automatic matchmaking with human expectations in the domain of apartment-rental advertisements. Apartment-rental matching requires tradeoffs among alternatives that vary along several preference dimensions, so it is a good demonstration of the richness of the matchmaking system.
“A Software Framework for Matchmaking Based on Semantic Web Technology,” by Lei Li and Ian Horrocks, explains how semantic Web features can be used for e-commerce. The paper includes a brief explanation of the features available in semantic Web languages, presents a particular approach to a service-description language, and describes experiments about the effectiveness of the language and an accompanying matchmaking protocol in a simulated marketplace.
A third representation-oriented paper is “SweetDeal: Representing Agent Contracts with Exceptions Using Semantic Web Rules, Ontologies, and Process Descriptions,” by Benjamin N. Grosof and Terrence C. Poon. The authors describe a system for electronic contracts that enables various modes of reasoning about contract execution. In particular, their SweetDeal system supports description of what happens when something goes wrong with the delivery of the contracted service. The paper includes several examples of how exceptions can be specified and handled in an electronic contract.
“On-line Trading of Rights-Enabled Learning Objects,” by Renato Iannella, focuses on the requirements of a standard framework for buying and selling of learning objects (LOs). LOs are computer media created by universities or companies to help students more effectively learn a specific type of material. LOs range from electronic books to software programs. The Learning Object Exchange ran for over a year, and facilitated the trading of LOs by providing a marketplace where buyers and sellers could buy and sell rights-enabled LOs that had attached descriptions of the permitted sharing policies. The trading of LOs has gone beyond the question of which cryptographic techniques are sufficient to the questions of what policies are appropriate and how policies should be specified. The hope is to enable a rich new B2B marketplace for learning objects. Recommender systems comprise a promising e-commerce technology that has already demonstrated results in the marketplace. Recommender systems help buyers find products they wish to purchase from among the large universe of available products on a Web site. Collaborative filtering is an important recommender-system technology with the goal of automatically computing recommendations based on past user behavior. Collaborative filtering compares individual behavior with group behavior to predict future individual behavior.
“Similarity Measure and Instance Selection for Collaborative Filtering,” by Chun Zeng, Chun-Xiao Xing, Li-Zhu Zhou, and Xiao-Hui Zheng, describes a powerful improvement to a collaborative filtering system that uses a much smaller set of data about past group behavior for its predictions. Remarkably, not only are the predictions more efficient when they are based on fewer data, but they are also more accurate.
We hope that you will enjoy these papers as much as we did and that they will stimulate your own research and practice in e-commerce matchmaking.
MICHAEL P. WELLMAN (email@example.com) is a professor of computer science and engineering, and director of the Artificial Intelligence Laboratory at the University of Michigan. He received a Ph.D. from the Massachusetts Institute of Technology in 1988 for his work in qualitative probabilistic reasoning and decision-theoretic planning. >From 1988 to 1992, Dr. Wellman conducted research in these areas at the U.S. Air Force’s Wright Laboratory. For the past decade, his research has focused on computational market mechanisms for distributed decision-making and electronic commerce. As chief market technologist for TradingDynamics, Inc. (now part of Ariba), he designed configurable auction technology for dynamic business-to-business commerce. Dr. Wellman is chair of the ACM Special Interest Group on Electronic Commerce (SIGecom), and previously served as executive editor of the Journal of Artificial Intelligence Research. He has been elected councilor and fellow of the American Association for Artificial Intelligence.
JOHN RIEDL (firstname.lastname@example.org) has been a member of the faculty of the Computer Science Department of the University of Minnesota since March 1990. In 1992, he co-founded the GroupLens Research project on collaborative information filtering, and he has been co-directing it ever since. In 1996, he co-founded Net Perceptions to commercialize GroupLens. Net Perceptions was the leading one-to-one real-time marketing company for several years. Dr. Riedl is associate professor at the University of Minnesota, and director and chief scientist at Net Perceptions. In 1999, he and other Net Perceptions co-founders shared the MIT Sloan School’s award for E-Commerce Technology. They also shared the World Technology Award as the individual leaders worldwide who most contributed to the advance of emerging technologies for the benefit of business and society. Dr. Riedl received a bachelor’s degree in mathematics from the University of Notre Dame in 1983. He earned a master’s degree in computer science in 1985 and a doctorate in computer science in 1990 from Purdue University.