TY - GEN
T1 - Multi-attribute dynamic pricing for online markets using intelligent agents
AU - Dasgupta, Prithviraj
AU - Hashimoto, Yoshitsugu
PY - 2004
Y1 - 2004
N2 - Intelligent agents called pricebots provide a conviennent mechanism for implementing automated dynamic pricing algorithms for sellers in an online economy. Pricebots enable an online seller to dynamically calculate a competitive price for a product in response to variations in market parameters such as competitors' prices and consumers' purchase preferences. Previous research on pricebot mediated pricing makes certain simplifying assumptions of on-line markets such as providing sellers with complete knowledge of market parameters tofacililate calculations by the dynamic pricing algorithm, and, considering product price as the only attribute that determines consumers' purchase decision. In this paper, we address the problem of dynamic pricing in a competitive online economy where a product is differentiated by buyers and sellers on multiple attributes, and, sellers possess limited knowledge about market parameters. A seller uses a collaborative filtering algorithm to determine temporal consumers' purchase preferences followed by a dynamic pricing algorithm to determine a competitive price for the product. Simulation results using our market model show that collaborative filtering enabled dynamic pricing techniques compare favorably against other dynamic pricing algorithms. Collaborative filtering enables sellers to rapidly identify temporal customer preferences and improve sellers' profits.
AB - Intelligent agents called pricebots provide a conviennent mechanism for implementing automated dynamic pricing algorithms for sellers in an online economy. Pricebots enable an online seller to dynamically calculate a competitive price for a product in response to variations in market parameters such as competitors' prices and consumers' purchase preferences. Previous research on pricebot mediated pricing makes certain simplifying assumptions of on-line markets such as providing sellers with complete knowledge of market parameters tofacililate calculations by the dynamic pricing algorithm, and, considering product price as the only attribute that determines consumers' purchase decision. In this paper, we address the problem of dynamic pricing in a competitive online economy where a product is differentiated by buyers and sellers on multiple attributes, and, sellers possess limited knowledge about market parameters. A seller uses a collaborative filtering algorithm to determine temporal consumers' purchase preferences followed by a dynamic pricing algorithm to determine a competitive price for the product. Simulation results using our market model show that collaborative filtering enabled dynamic pricing techniques compare favorably against other dynamic pricing algorithms. Collaborative filtering enables sellers to rapidly identify temporal customer preferences and improve sellers' profits.
KW - Agent mediated e-commerce
KW - Collaborative filtering
KW - Dynamic pricing
UR - http://www.scopus.com/inward/record.url?scp=4544365238&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:4544365238
SN - 1581138644
T3 - Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 2004
SP - 277
EP - 284
BT - Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagents Systems, AAMAS 2004
A2 - Jennings, N.R.
A2 - Sierra, C.
A2 - Sonenberg, L.
A2 - Tambe, M.
T2 - Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 2004
Y2 - 19 July 2004 through 23 July 2004
ER -