TY - JOUR
T1 - Generalized ensemble model for document ranking in information retrieval
AU - Wang, Yanshan
AU - Choi, In Chan
AU - Liu, Hongfang
N1 - Publisher Copyright:
© 2017, ComSIS Consortium. All rights reserved.
PY - 2017/1
Y1 - 2017/1
N2 - 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.
AB - 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.
KW - Document ranking
KW - Ensemble model
KW - Information retrieval
KW - Mean average precision
KW - Optimization
UR - http://www.scopus.com/inward/record.url?scp=85011634648&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85011634648&partnerID=8YFLogxK
U2 - 10.2298/CSIS160229042W
DO - 10.2298/CSIS160229042W
M3 - Article
AN - SCOPUS:85011634648
SN - 1820-0214
VL - 14
SP - 123
EP - 151
JO - Computer Science and Information Systems
JF - Computer Science and Information Systems
IS - 1
ER -