Multi-attribute dynamic pricing for online markets using intelligent agents

Intelligent agents called pricebots provide a convienient 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 competitorsý prices and consumersý purchase preferences. Previous research on pricebot mediated pricing makes certain simplifying assumptions of online markets such as providing sellers with complete knowledge of market parameters to facililate calculations by the dynamic pricing algorithm, and, considering product price as the only attribute that determines consumersý 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 consumersý 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 sellersý profits.

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