Snippet-based relevance predictions for federated web search

Thomas Demeester, Dong-Phuong Nguyen, Rudolf Berend Trieschnigg, Chris Develder, Djoerd Hiemstra

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

7 Citations (Scopus)
44 Downloads (Pure)


How well can the relevance of a page be predicted, purely based on snippets? This would be highly useful in a Federated Web Search setting where caching large amounts of result snippets is more feasible than caching entire pages. The experiments reported in this paper make use of result snippets and pages from a diverse set of actual Web search engines. A linear classifier is trained to predict the snippet-based user estimate of page relevance, but also, to predict the actual page relevance, again based on snippets alone. The presented results confirm the validity of the proposed approach and provide promising insights into future result merging strategies for a Federated Web Search setting.
Original languageUndefined
Title of host publicationAdvances in Information Retrieval, Proceedings of the 35th European Conference on IR Research, ECIR 2013
Place of PublicationBerlin
Number of pages4
ISBN (Print)978-3-642-36972-8
Publication statusPublished - Mar 2013
Event35th European Conference on Information Retrieval, ECIR 2013: (IR Resarch) - Moscow, Russian Federation
Duration: 24 Mar 201327 Mar 2013
Conference number: 35

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Verlag
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference35th European Conference on Information Retrieval, ECIR 2013
Abbreviated titleECIR
Country/TerritoryRussian Federation


  • EWI-24058
  • METIS-300200
  • IR-88458

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