Generalized Ensemble Model for Document Ranking in Information Retrieval

Yanshan Wang1, In-Chan Choi2 and Hongfang Liu1

  1. Department of Health Sciences Research, Mayo Clinic
    Rochester, MN 55905, USA
    {wang.yanshan, liu.hongfang}@mayo.edu
  2. School of Industrial Management Engineering, Korea University
    Seoul 136-701, South Korea
    ichoi@korea.edu

Abstract

A generalized ensemble model (gEnM) for document ranking is proposed in this paper. The gEnM linearly combines the document retrieval models and tries to retrieve relevant documents at high positions. In order to obtain the optimal linear combination of multiple document retrieval models or rankers, an optimization program is formulated by directly maximizing the mean average precision. Both supervised and unsupervised learning algorithms are presented to solve this program. For the supervised scheme, two approaches are considered based on the data setting, namely batch and online setting. In the batch setting, we propose a revised Newton’s algorithm, gEnM.BAT, by approximating the derivative and Hessian matrix. In the online setting, we advocate a stochastic gradient descent (SGD) based algorithm—gEnM.ON. As for the unsupervised scheme, an unsupervised ensemble model (UnsEnM) by iteratively co-learning from each constituent ranker is presented. Experimental study on benchmark data sets verifies the effectiveness of the proposed algorithms. Therefore, with appropriate algorithms, the gEnM is a viable option in diverse practical information retrieval applications.

Key words

information retrieval, optimization, mean average precision, document ranking, ensemble model

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS160229042W

Publication information

Volume 14, Issue 1 (January 2017)
Year of Publication: 2017
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Wang, Y., Choi, I., Liu, H.: Generalized Ensemble Model for Document Ranking in Information Retrieval. Computer Science and Information Systems, Vol. 14, No. 1, 123–151. (2017)