Generalized Ensemble Model for Document Ranking in Information Retrieval

TL;DR

This paper introduces a generalized ensemble model (gEnM) for document ranking that optimally combines multiple retrieval models to improve relevance ranking, utilizing both supervised and unsupervised learning algorithms, validated on benchmark datasets.

Contribution

The paper presents a novel generalized ensemble model for document ranking, with new algorithms for optimal linear combination using both supervised (batch and online) and unsupervised methods.

Findings

gEnM outperforms individual models on benchmark datasets

Supervised algorithms achieve higher accuracy than unsupervised methods

The proposed methods are effective in diverse information retrieval scenarios

Abstract

A generalized ensemble model (gEnM) for document ranking is proposed in this paper. The gEnM linearly combines basis 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.