TY - GEN
T1 - Eliciting buyer preferences using intelligent agents for multi-attribute dynamic pricing
AU - Dasgupta, Prithviraj
AU - Hashimoto, Yoshitsugu
PY - 2004
Y1 - 2004
N2 - We address the problem of determining buyer preferences for efficient dynamic pricing by sellers in a competitive online market. Prior reserch on online dynamic pricing by sellers makes certain limiting assumptions such as sellers being aware of buyers preferences, and, prices and profits of competitors, and, buyers selecting products based only on their price. In this paper, we consider a market where buyers and sellers differentiate a product on multiple attributes, and, preferences of different buyers over different product attributes vary temporally. We describe adaptive learning and dynamic pricing algorithms that can be used by a seller to determine buyer preferences and determine a competitive price in the market. These algorithms are encapsulated by software agents that automatically perform the necessary calculations so that the seller can maintain a competitive edge in the market. Simulation results of our algorithms show that they compare favorably against other existing online dynamic pricing algorithms.
AB - We address the problem of determining buyer preferences for efficient dynamic pricing by sellers in a competitive online market. Prior reserch on online dynamic pricing by sellers makes certain limiting assumptions such as sellers being aware of buyers preferences, and, prices and profits of competitors, and, buyers selecting products based only on their price. In this paper, we consider a market where buyers and sellers differentiate a product on multiple attributes, and, preferences of different buyers over different product attributes vary temporally. We describe adaptive learning and dynamic pricing algorithms that can be used by a seller to determine buyer preferences and determine a competitive price in the market. These algorithms are encapsulated by software agents that automatically perform the necessary calculations so that the seller can maintain a competitive edge in the market. Simulation results of our algorithms show that they compare favorably against other existing online dynamic pricing algorithms.
KW - Dynamic pricing
KW - E-commerce
KW - K-means algorithm
KW - Online seller
UR - http://www.scopus.com/inward/record.url?scp=12744254260&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=12744254260&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:12744254260
SN - 1932415335
SN - 9781932415339
T3 - Proceedings of the International Conference on Artificial Intelligence, IC-AI'04
SP - 390
EP - 396
BT - Proceedings of the International Conference on Artificial Intelligence, IC-AI'04
A2 - Arabnia, H.R.
T2 - Proceedings of the International Conference on Artificial Intelligence, IC-AI'04
Y2 - 21 June 2004 through 24 June 2004
ER -