Improving similar document retrieval using a recursive pseudo relevance feedback strategy

Kyle Williams, C. Lee Giles

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

We present a recursive pseudo relevance feedback strategy for improving retrieval performance in similarity search. The strategy recursively searches on search results returned for a given query and produces a tree that is used for ranking. Experiments on the Reuters 21578 and WebKB datasets show how the strategy leads to a significant improvement in similarity search performance.

Original languageEnglish (US)
Title of host publicationJCDL 2016 - Proceedings of the 16th ACM/IEEE-CS Joint Conference on Digital Libraries
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages275-276
Number of pages2
ISBN (Electronic)9781450342292
DOIs
StatePublished - Sep 1 2016
Event16th ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL 2016 - Newark, United States
Duration: Jun 19 2016Jun 23 2016

Publication series

NameProceedings of the ACM/IEEE Joint Conference on Digital Libraries
Volume2016-September
ISSN (Print)1552-5996

Other

Other16th ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL 2016
Country/TerritoryUnited States
CityNewark
Period6/19/166/23/16

All Science Journal Classification (ASJC) codes

  • General Engineering

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