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EWCBR
2004
Springer

Improving Recommendation Ranking by Learning Personal Feature Weights

13 years 9 months ago
Improving Recommendation Ranking by Learning Personal Feature Weights
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.
Lorcan Coyle, Padraig Cunningham
Added 01 Jul 2010
Updated 01 Jul 2010
Type Conference
Year 2004
Where EWCBR
Authors Lorcan Coyle, Padraig Cunningham
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