Snippet-Based Relevance Predictions for Federated Web Search

Thomas Demeester, Dong Nguyen, Dolf Trieschnigg, Chris Develder, Djoerd Hiemstra

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

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 languageEnglish
Title of host publicationAdvances in Information Retrieval
Subtitle of host publication35th European Conference on IR Research, ECIR 2013, Moscow, Russia, March 24-27, 2013. Proceedings
EditorsPavel Serdyukov, Pavel Braslavski, Sergei O. Kuznetsov, Jaap Kamps, Stefan Rüger, Eugene Agichtein, Ilya Segalovich, Emine Yilmaz
Place of PublicationBerlin, Heidelberg
PublisherSpringer Berlin Heidelberg
Pages697-700
Number of pages4
ISBN (Electronic)978-3-642-36973-5
ISBN (Print)978-3-642-36972-8
DOIs
Publication statusPublished - 2013

Publication series

NameLecture Notes in Computer Science (LNCS)
PublisherSpringer Berlin Heidelberg
Volume7814
ISSN (Print)0302-9743

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