Abstract
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.
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Somefun, D.J.A., La Poutré, J.A. (2007). A Fast Method for Learning Non-linear Preferences Online Using Anonymous Negotiation Data. In: Fasli, M., Shehory, O. (eds) Agent-Mediated Electronic Commerce. Automated Negotiation and Strategy Design for Electronic Markets. TADA AMEC 2006 2006. Lecture Notes in Computer Science(), vol 4452. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72502-2_9
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DOI: https://doi.org/10.1007/978-3-540-72502-2_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72501-5
Online ISBN: 978-3-540-72502-2
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