Automated strategy searches in an electronic goods market: learning and complex price schedules
Proceedings of the 1st ACM conference on Electronic commerce
Dynamic pricing by software agents
Computer Networks: The International Journal of Computer and Telecommunications Networking - electronic commerce
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Proceedings of the fifth international conference on Autonomous agents
Winner determination in combinatorial auction generalizations
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Pricing in Agent Economies Using Multi-Agent Q-Learning
Autonomous Agents and Multi-Agent Systems
Dynamic Consumer Profiling and Tiered Pricing Using Software Agents
Electronic Commerce Research
Convergent algorithms for collaborative filtering
Proceedings of the 4th ACM conference on Electronic commerce
Improving learning performance by applying economic knowledge
Proceedings of the 4th ACM conference on Electronic commerce
Bidders' strategy for multi-attribute sequential english auction with a deadline
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Dynamic Service Pricing for Brokers in a Multi-Agent Economy
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Market-Based Distributed Task Selection in Multi-agent Swarms
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
Dynamic Pricing Algorithms for Task Allocation in Multi-agent Swarms
Massively Multi-Agent Technology
Adaptive conceding strategies for automated trading agents in dynamic, open markets
Decision Support Systems
Proceedings of the 11th International Conference on Electronic Commerce
PRIMA '09 Proceedings of the 12th International Conference on Principles of Practice in Multi-Agent Systems
GECON'10 Proceedings of the 7th international conference on Economics of grids, clouds, systems, and services
A dynamic pricing approach in e-commerce based on multiple purchase attributes
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
A parallel, multi-issue negotiation model in dynamic e-markets
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Impact of pricing schemes on a market for Software-as-a-Service and perpetual software
Future Generation Computer Systems
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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.