Local search: A guide for the information retrieval practitioner

被引:5
|
作者
MacFarlane, Andrew [1 ]
Tuson, Andrew [2 ]
机构
[1] City Univ London, Sch Informat, Dept Informat Sci, London EC1V 0HB, England
[2] City Univ London, Sch Informat, Dept Comp, London EC1V 0HB, England
关键词
Combinatorial optimisation; Information retrieval; Local search; Evaluation; GENETIC ALGORITHMS; RELEVANCE FEEDBACK; FITNESS FUNCTIONS; BOOLEAN QUERIES; OPTIMIZATION; DISCOVERY; CONVERGENCE; AGENT;
D O I
10.1016/j.ipm.2008.09.002
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
There are a number of combinatorial optimisation problems in information retrieval in which the use of local search methods are worthwhile. The purpose of this paper is to show how local search can be used to solve some well known tasks in information retrieval (IR), how previous research in the field is piecemeal, bereft of a structure and methodologically flawed, and to suggest more rigorous ways of applying local search methods to solve IR problems. We provide a query based taxonomy for analysing the use of local search in IR tasks and an overview of issues such as fitness functions, statistical significance and test collections when conducting experiments on combinatorial optimisation problems. The paper gives a guide on the pitfalls and problems for IR practitioners who wish to use local search to solve their research issues, and gives practical advice on the use of such methods. The query based taxonomy is a novel structure which can be used by the IR practitioner in order to examine the use of local search in IR. (C) 2008 Elsevier Ltd. All rights reserved.
引用
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页码:159 / 174
页数:16
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