Journal article
A generic ranking function discovery framework by genetic programming for information retrieval
Information processing & management, Vol.40(4), pp.587-602
07/01/2004
DOI: 10.1016/j.ipm.2003.08.001
Abstract
Ranking functions play a substantial role in the performance of information retrieval (IR) systems and search engines. Although there are many ranking functions available in the IR literature, various empirical evaluation studies show that ranking functions do not perform consistently well across different contexts (queries, collections, users). Moreover, it is often difficult and very expensive for human beings to design optimal ranking functions that work well in all these contexts. In this paper, we propose a novel ranking function discovery framework based on Genetic Programming and show through various experiments how this new framework helps automate the ranking function design/discovery process.
Details
- Title: Subtitle
- A generic ranking function discovery framework by genetic programming for information retrieval
- Creators
- Weiguo Fan - Virginia TechMichael D Gordon - University of Michigan–Ann ArborPraveen Pathak - University of Florida
- Resource Type
- Journal article
- Publication Details
- Information processing & management, Vol.40(4), pp.587-602
- Publisher
- Elsevier Ltd
- DOI
- 10.1016/j.ipm.2003.08.001
- ISSN
- 0306-4573
- eISSN
- 1873-5371
- Language
- English
- Date published
- 07/01/2004
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380549802771
Metrics
5 Record Views