@inproceedings{ed472b56fc5b46c9a46a7b13b59c1a8a,
title = "A fast method for learning non-linear preferences online using anonymous negotiation data",
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{\textquoteright}s search for mutually beneficial alternative bundles, we develop a method for online learning customers{\textquoteright} preferences, while respecting their privacy. By introducing additional parameters, we represent customers{\textquoteright} 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{\textquoteright}s pricing strategy, and it performs well even if customers behave strategically.",
author = "D.J.A. Somefun and {Poutr{\'e}, La}, J.A.",
year = "2007",
doi = "10.1007/978-3-540-72502-2_9",
language = "English",
isbn = "978-3-540-72501-5",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "118--131",
editor = "M. Fasli and O. Shehory",
booktitle = "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",
address = "Germany",
note = "5th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2006), May 8–12, 2006, Hakodate, Japan, AAMAS 2006 ; Conference date: 08-05-2006 Through 12-05-2006",
}