Pricing Agents for a Group Buying System

Internet group buying systems have been widely used recently. In those systems, because the reserve price is provided by the buyer, the success rate can be decreased if the reserve price is set too low compared with the normal price. Otherwise, an unsuitable successful bid can be made if the reserve price is set too high based on inaccurate information. Likewise, the seller's providing too high a bid price can deteriorate his/her own successful bid rate, whereas a successful bid with too low a price may make no profit in the sale. Therefore, pricing agents that recommend adequate prices based on the past buying and selling history data can be helpful. In this paper, we propose two kinds of agents. One suggests reserve prices to buyers based on the past buying history database of the system. The other recommends bid prices to a seller based on the past bidding history data of the company using the cost accounting theory. Through performance experiments, we show that the successful bid rate can increase by preventing buyers from making unreasonable reserve prices. Also, we show that, for the seller, the rate of successful bids with appropriate profits can increase. Using the pricing agents, we design and implement an XML-based group buying system. Because it is based on XML standards, it has advantages such as interoperability and extendibility compared with previous proprietary electronic commerce systems.

[1]  A. Messier,et al.  Cost Accounting. , 1939, American journal of public health and the nation's health.

[2]  R. McAfee,et al.  Auctions and Bidding , 1986 .

[3]  Ricardo Baeza-Yates,et al.  Information Retrieval: Data Structures and Algorithms , 1992 .

[4]  Venkata Subramaniam,et al.  Information Retrieval: Data Structures & Algorithms , 1992 .

[5]  Robert J. Kauffman,et al.  Information systems and economics , 1998, CACM.

[6]  John Riedl,et al.  Recommender systems in e-commerce , 1999, EC '99.

[7]  Pattie Maes,et al.  Agents that buy and sell , 1999, CACM.

[8]  John Riedl,et al.  Analysis of recommendation algorithms for e-commerce , 2000, EC '00.

[9]  Rajarshi Das,et al.  Dynamic service pricing for brokers in a multi-agent economy , 2000, Proceedings Fourth International Conference on MultiAgent Systems.

[10]  Alok Gupta,et al.  Simulating online Yankee auctions to optimize sellers revenue , 2001, Proceedings of the 34th Annual Hawaii International Conference on System Sciences.

[11]  Bin Wang,et al.  New buyers' arrival under dynamic pricing market microstructure: the case of group-buying discounts on the Internet , 2001, Proceedings of the 34th Annual Hawaii International Conference on System Sciences.

[12]  Ravi Kumar,et al.  Recommendation Systems , 2001 .

[13]  Tsuneaki Kato,et al.  An expert recommendation system using concept-based relevance discernment , 2001, Proceedings 13th IEEE International Conference on Tools with Artificial Intelligence. ICTAI 2001.

[14]  Elliotte Rusty Harold,et al.  XML in a Nutshell , 2001 .

[15]  Alok Gupta,et al.  Insights and analyses of online auctions , 2001, CACM.