Web query reformulation using differential evolution

Authors:
Prabhat K. Mahanti;Mohammad Al-Fayoumi;Soumya Banerjee;Feras Al-Obeidat
Affiliations:
Department of CSAS, University of New Brunswick, New Brunswick, Canada;King Abdul Aziz University, Faculty of Computing & Information Technology, Saudi Arabia;Department of Information Technology, Birla Institute of Technology Mesra, India;Department of CSAS, University of New Brunswick, New Brunswick, Canada
Venue:
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
Year:
2010

Citing 10
Cited 0

Patterns of search: analyzing and modeling Web query refinement

UM '99 Proceedings of the seventh international conference on User modeling
Community search assistant

Proceedings of the 6th international conference on Intelligent user interfaces
A new cluster validity measure and its application to image compression

Pattern Analysis & Applications
Query chains: learning to rank from implicit feedback

Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Computational Intelligence: Principles, Techniques and Applications

Computational Intelligence: Principles, Techniques and Applications
Random walks on the click graph

SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Graphs from Search Engine Queries

SOFSEM '07 Proceedings of the 33rd conference on Current Trends in Theory and Practice of Computer Science
The query-flow graph: model and applications

Proceedings of the 17th ACM conference on Information and knowledge management
Query suggestions using query-flow graphs

Proceedings of the 2009 workshop on Web Search Click Data
Analysis of multiple query reformulations on the web: The interactive information retrieval context

Information Processing and Management: an International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a query reformulation and clustering technique using Differential Evolution. Differential evolution (DE) has emerged as one of the fast, robust, and efficient global search heuristics of current interest. The proposed DE automatically determines the type of a query and new pattern of query reformulation.