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Please use this identifier to cite or link to this item: http://hdl.handle.net/2262/13260

Title: Improving Recommendation Ranking by Learning Personal Feature Weights
Author: Coyle, Lorcan
Cunningham, Pádraig
Sponsor: Science Foundation Ireland
Enterprise Ireland
Keywords: Computer Science
Issue Date: 24-Jun-2004
Publisher: Trinity College Dublin, Department of Computer Science
Citation: Coyle, Lorcan; Cunningham, Pádraig. 'Improving Recommendation Ranking by Learning Personal Feature Weights'. - Dublin, Trinity College Dublin, Department of Computer Science, TCD-CS-2004-21, 2004, pp13
Series/Report no.: Computer Science Technical Report
TCD-CS-2004-21
Abstract: The ranking of offers is an issue in e-commerce that has received a lot of attention in Case-Based Reasoning research. In the absence of a sales assistant, it is important to provide a facility that will bring suitable products and services to the attention of the customer. In this paper we present such a facility that is part of a Personal Travel Assistant (PTA) for booking flights online. The PTA returns a large number of offers (24 on average) and it is important to rank them to bring the most suitable to the fore. This ranking is done based on similarity to previously accepted offers. It is a characteristic of this domain that the case-base of accepted offers will be small, so the learning of appropriate feature weights is a particular challenge. We describe a process for learning personalised feature weights and present an evaluation that shows its effectiveness.
URI: https://www.cs.tcd.ie/publications/tech-reports/reports.04/TCD-CS-2004-21.pdf
http://hdl.handle.net/2262/13260
Appears in Collections:Computer Science Technical Reports

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