A fast method for learning non-linear preferences online using anonymous negotiation data
| Autor: | Somefun, D.J.A., Poutré, La, J.A., Fasli, M., Shehory, O. |
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| Přispěvatelé: | Information Systems IE&IS |
| Jazyk: | angličtina |
| Rok vydání: | 2007 |
| Předmět: |
Operations research
Computer science Stochastic process business.industry Online learning Customer preference media_common.quotation_subject Linear model Computer experiment Machine learning computer.software_genre Nonlinear system Negotiation Bundle Artificial intelligence business computer media_common |
| Zdroj: | Selected and revised papers of the Agent mediated electronic commerce: automated negotiation and strategy design for electronic markets (AAMAS 2006 Workshop, TADA/AMEC 2006) 9 May 2006, Hakodate, Japan, 118-131 STARTPAGE=118;ENDPAGE=131;TITLE=Selected and revised papers of the Agent mediated electronic commerce: automated negotiation and strategy design for electronic markets (AAMAS 2006 Workshop, TADA/AMEC 2006) 9 May 2006, Hakodate, Japan Lecture Notes in Computer Science ISBN: 9783540725015 TADA/AMEC |
| Popis: | In this paper, we consider the problem of a shop agent negotiating bilaterally with many customers about a bundle of goods or services together with a price. To facilitate the shop agent's search for mutually beneficial alternative bundles, we develop a method for online learning customers' preferences, while respecting their privacy. By introducing additional parameters, we represent customers' highly nonlinear preferences as a linear model. We develop a method for learning the underlying stochastic process of these parameters online. As the conducted computer experiments show, the developed method has a number of advantages: it scales well, the acquired knowledge is robust towards changes in the shop's pricing strategy, and it performs well even if customers behave strategically. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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