Paper Info
Title
Discovery of Context-Specific Ranking Functions for Effective Information Retrieval Using Genetic Programming
Abstract
Abstract--The Internet and corporate Intranets have brought a lot of information. People usually resort to search engines to find required information. However, these systems tend to use only one fixed ranking strategy regardless of the contexts. This poses serious performance problems when characteristics of different users, queries, and text collections are taken into account. In this paper, we argue that the ranking strategy should be context specific and we propose a new systematic method that can automatically generate ranking strategies for different contexts based on Genetic Programming (GP). The new method was tested on TREC data and the results are very promising.
Year
DOI
Venue
2004
10.1109/TKDE.2004.1269663
IEEE Trans. Knowl. Data Eng.
Keywords
Field
DocType
genetic programming,trec data,ranking strategy,contextual information retrieval,effective information retrieval,search engine,new systematic method,serious performance problem,context-specific ranking functions,ranking function,different context,information retrieval,personalization,text mining,intelligent information retrieval,different user,corporate intranets,new method,information routing,fixed ranking strategy,term weighting,documentation,tree data structures,genetic algorithms,testing,information systems,search engines,internet,routing,data mining
Information system,Data mining,Computer science,Genetic programming,Ranking (information retrieval),Artificial intelligence,Genetic algorithm,Personalization,The Internet,Information retrieval,Ranking,Tree (data structure),Machine learning
Journal
Volume
Issue
ISSN
16
4
1041-4347
Citations 
PageRank 
References 
59
2.25
15
Authors
3
Name
Order
Citations
PageRank
Weiguo Fan12055133.38
Michael D. Gordon2105199.36
Praveen Pathak361438.38