Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.ipm.2008.09.002 | Inf. Process. Manage. |
Keywords | Field | DocType |
information retrieval,combinatorial optimisation problem,ir task,novel structure,ir practitioner,local search method,information retrieval practitioner,ir problem,local search,combinatorial optimisation,evaluation,research issue,previous research,statistical significance,fitness function | Data mining,Search engine,Information retrieval,Computer science,Combinatorial optimization,Local search (optimization) | Journal |
Volume | Issue | ISSN |
45 | 1 | Information Processing and Management |
Citations | PageRank | References |
2 | 0.37 | 50 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrew Macfarlane | 1 | 148 | 17.18 |
| Andrew Tuson | 2 | 152 | 16.65 |