Risk-Sensitive Evaluation and Learning to Rank using Multiple Baselines

Dinçer, B. T., Macdonald, C. and Ounis, I. (2016) Risk-Sensitive Evaluation and Learning to Rank using Multiple Baselines. In: SIGIR 2016, Pisa, Italy, 17-21 July 2016, pp. 483-492. ISBN 9781450340694 (doi:10.1145/2911451.2911511)

Dinçer, B. T., Macdonald, C. and Ounis, I. (2016) Risk-Sensitive Evaluation and Learning to Rank using Multiple Baselines. In: SIGIR 2016, Pisa, Italy, 17-21 July 2016, pp. 483-492. ISBN 9781450340694 (doi:10.1145/2911451.2911511)

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Abstract

A robust retrieval system ensures that user experience is not damaged by the presence of poorly-performing queries. Such robustness can be measured by risk-sensitive evaluation measures, which assess the extent to which a system performs worse than a given baseline system. However, using a particular, single system as the baseline suffers from the fact that retrieval performance highly varies among IR systems across topics. Thus, a single system would in general fail in providing enough information about the real baseline performance for every topic under consideration, and hence it would in general fail in measuring the real risk associated with any given system. Based upon the Chi-squared statistic, we propose a new measure ZRisk that exhibits more promise since it takes into account multiple baselines when measuring risk, and a derivative measure called GeoRisk, which enhances ZRisk by also taking into account the overall magnitude of effectiveness. This paper demonstrates the benefits of ZRisk and GeoRisk upon TREC data, and how to exploit GeoRisk for risk-sensitive learning to rank, thereby making use of multiple baselines within the learning objective function to obtain effective yet risk-averse/robust ranking systems. Experiments using 10,000 topics from the MSLR learning to rank dataset demonstrate the efficacy of the proposed Chi-square statistic-based objective function.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Macdonald, Dr Craig and Ounis, Professor Iadh
Authors: Dinçer, B. T., Macdonald, C., and Ounis, I.
College/School:College of Science and Engineering > School of Computing Science
ISBN:9781450340694
Copyright Holders:Copyright © 2016 Association for Computing Machinery
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher
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