A fast method for learning non-linear preferences online using anonymous negotiation data

D.J.A. Somefun, J.A. Poutré, La

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademic

5 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationSelected 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
EditorsM. Fasli, O. Shehory
Place of PublicationBerlin
PublisherSpringer
Pages118-131
ISBN (Print)978-3-540-72501-5
DOIs
Publication statusPublished - 2007
Eventconference; AAMAS 2006 Workshop, TADA/AMEC 2006, Hakodate, Japan; 2006-05-09; 2006-05-09 -
Duration: 9 May 20069 May 2006

Publication series

NameLecture Notes in Computer Science
Volume4452
ISSN (Print)0302-9743

Conference

Conferenceconference; AAMAS 2006 Workshop, TADA/AMEC 2006, Hakodate, Japan; 2006-05-09; 2006-05-09
Period9/05/069/05/06
OtherAAMAS 2006 Workshop, TADA/AMEC 2006, Hakodate, Japan

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