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 | Undefined |
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Title of host publication | Advances in Information Retrieval, Proceedings of the 35th European Conference on IR Research, ECIR 2013 |
Place of Publication | Berlin |
Publisher | Springer |
Pages | 697-700 |
Number of pages | 4 |
ISBN (Print) | 978-3-642-36972-8 |
DOIs | |
Publication status | Published - Mar 2013 |
Event | 35th European Conference on Information Retrieval, ECIR 2013: (IR Resarch) - Moscow, Russian Federation Duration: 24 Mar 2013 → 27 Mar 2013 Conference number: 35 |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer Verlag |
Volume | 7814 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 35th European Conference on Information Retrieval, ECIR 2013 |
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Abbreviated title | ECIR |
Country/Territory | Russian Federation |
City | Moscow |
Period | 24/03/13 → 27/03/13 |
Keywords
- EWI-24058
- METIS-300200
- IR-88458