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Improving Recommendation Ranking by Learning Personal Feature Weights

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Advances in Case-Based Reasoning (ECCBR 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3155))

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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.

The support of the Informatics Research Initiative of Enterprise Ireland and the support of Science Foundation Ireland under grant No. 02/IN.1/I1111 are gratefully acknowledged.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Trinity College, Dublin

    Lorcan Coyle & Pádraig Cunningham

Authors
  1. Lorcan Coyle
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  2. Pádraig Cunningham
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Electronics, Mälardalen University, SE-72123, Västerås, Sweden

    Peter Funk

  2. Facultad de Informática, Universidad Complutense de Madrid, 28040, Madrid, Spain

    Pedro A. González Calero

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Coyle, L., Cunningham, P. (2004). Improving Recommendation Ranking by Learning Personal Feature Weights. In: Funk, P., González Calero, P.A. (eds) Advances in Case-Based Reasoning. ECCBR 2004. Lecture Notes in Computer Science(), vol 3155. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28631-8_41

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  • DOI: https://doi.org/10.1007/978-3-540-28631-8_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22882-0

  • Online ISBN: 978-3-540-28631-8

  • eBook Packages: Springer Book Archive

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