Snippet-Based Relevance Predictions for Federated Web Search

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

doi:10.1007/978-3-642-36973-5_63

Snippet-Based Relevance Predictions for Federated Web Search

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

School of Informatics

Research output: Chapter in Book/Report/Conference proceeding › Chapter

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 language	English
Title of host publication	Advances in Information Retrieval
Subtitle of host publication	35th European Conference on IR Research, ECIR 2013, Moscow, Russia, March 24-27, 2013. Proceedings
Editors	Pavel Serdyukov, Pavel Braslavski, Sergei O. Kuznetsov, Jaap Kamps, Stefan Rüger, Eugene Agichtein, Ilya Segalovich, Emine Yilmaz
Place of Publication	Berlin, Heidelberg
Publisher	Springer Berlin Heidelberg
Pages	697-700
Number of pages	4
ISBN (Electronic)	978-3-642-36973-5
ISBN (Print)	978-3-642-36972-8
DOIs	https://doi.org/10.1007/978-3-642-36973-5_63
Publication status	Published - 2013

Publication series

Name	Lecture Notes in Computer Science (LNCS)
Publisher	Springer Berlin Heidelberg
Volume	7814
ISSN (Print)	0302-9743

Access to Document

10.1007/978-3-642-36973-5_63

https://link.springer.com/chapter/10.1007%2F978-3-642-36973-5_63

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Demeester, T., Nguyen, D., Trieschnigg, D., Develder, C., & Hiemstra, D. (2013). Snippet-Based Relevance Predictions for Federated Web Search. In P. Serdyukov, P. Braslavski, S. O. Kuznetsov, J. Kamps, S. Rüger, E. Agichtein, I. Segalovich, & E. Yilmaz (Eds.), Advances in Information Retrieval: 35th European Conference on IR Research, ECIR 2013, Moscow, Russia, March 24-27, 2013. Proceedings (pp. 697-700). (Lecture Notes in Computer Science (LNCS); Vol. 7814). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-36973-5_63

Demeester, Thomas ; Nguyen, Dong ; Trieschnigg, Dolf ; Develder, Chris ; Hiemstra, Djoerd. / Snippet-Based Relevance Predictions for Federated Web Search. Advances in Information Retrieval: 35th European Conference on IR Research, ECIR 2013, Moscow, Russia, March 24-27, 2013. Proceedings. editor / Pavel Serdyukov ; Pavel Braslavski ; Sergei O. Kuznetsov ; Jaap Kamps ; Stefan Rüger ; Eugene Agichtein ; Ilya Segalovich ; Emine Yilmaz. Berlin, Heidelberg : Springer Berlin Heidelberg, 2013. pp. 697-700 (Lecture Notes in Computer Science (LNCS)).

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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.",

author = "Thomas Demeester and Dong Nguyen and Dolf Trieschnigg and Chris Develder and Djoerd Hiemstra",

year = "2013",

doi = "10.1007/978-3-642-36973-5_63",

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Demeester, T, Nguyen, D, Trieschnigg, D, Develder, C & Hiemstra, D 2013, Snippet-Based Relevance Predictions for Federated Web Search. in P Serdyukov, P Braslavski, SO Kuznetsov, J Kamps, S Rüger, E Agichtein, I Segalovich & E Yilmaz (eds), Advances in Information Retrieval: 35th European Conference on IR Research, ECIR 2013, Moscow, Russia, March 24-27, 2013. Proceedings. Lecture Notes in Computer Science (LNCS), vol. 7814, Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 697-700. https://doi.org/10.1007/978-3-642-36973-5_63

Snippet-Based Relevance Predictions for Federated Web Search. / Demeester, Thomas; Nguyen, Dong; Trieschnigg, Dolf; Develder, Chris; Hiemstra, Djoerd.

Advances in Information Retrieval: 35th European Conference on IR Research, ECIR 2013, Moscow, Russia, March 24-27, 2013. Proceedings. ed. / Pavel Serdyukov; Pavel Braslavski; Sergei O. Kuznetsov; Jaap Kamps; Stefan Rüger; Eugene Agichtein; Ilya Segalovich; Emine Yilmaz. Berlin, Heidelberg : Springer Berlin Heidelberg, 2013. p. 697-700 (Lecture Notes in Computer Science (LNCS); Vol. 7814).

Research output: Chapter in Book/Report/Conference proceeding › Chapter

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AU - Develder, Chris

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AB - 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.

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Demeester T, Nguyen D, Trieschnigg D, Develder C, Hiemstra D. Snippet-Based Relevance Predictions for Federated Web Search. In Serdyukov P, Braslavski P, Kuznetsov SO, Kamps J, Rüger S, Agichtein E, Segalovich I, Yilmaz E, editors, Advances in Information Retrieval: 35th European Conference on IR Research, ECIR 2013, Moscow, Russia, March 24-27, 2013. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg. 2013. p. 697-700. (Lecture Notes in Computer Science (LNCS)). https://doi.org/10.1007/978-3-642-36973-5_63