Learning to rank using evolutionary computation: Immune programming or genetic programming?

Shuaiqiang Wang*, Jun Ma, Jiming LIU

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

11 Citations (Scopus)

Abstract

Nowadays ranking function discovery approaches using Evolutionary Computation (EC), especially Genetic Programming (GP), have become an important branch in the Learning to Rank for Information Retrieval (LR4IR) field. Inspired by the GP based learning to rank approaches, we provide a series of generalized definitions and a common framework for the application of EC in learning to rank research. Besides, according to the introduced framework, we propose RankIP, a ranking function discovery approach using Immune Programming (IP). Experimental results demonstrate that RankIP evidently outperforms the baselines. In addition, we study the differences between IP and GP in theory and experiments. Results show that IP is more suitable for LR4IR due to its high diversity.

Original languageEnglish
Title of host publicationACM 18th International Conference on Information and Knowledge Management, CIKM 2009
Pages1879-1882
Number of pages4
DOIs
Publication statusPublished - 2009
EventACM 18th International Conference on Information and Knowledge Management, CIKM 2009 - Hong Kong, China
Duration: 2 Nov 20096 Nov 2009

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

ConferenceACM 18th International Conference on Information and Knowledge Management, CIKM 2009
Country/TerritoryChina
CityHong Kong
Period2/11/096/11/09

Scopus Subject Areas

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

User-Defined Keywords

  • Evolutionary computation
  • Immune programming
  • Information retrieval
  • Learning to rank
  • Page ranking

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