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Demeester, T. and Nguyen, Dong-Phuong and Trieschnigg, R.B. and Develder, C. and Hiemstra, D. (2013) Snippet-based relevance predictions for federated web search. In: Advances in Information Retrieval, Proceedings of the 35th European Conference on IR Research, ECIR 2013, 24-27 Mar 2013, Moscow, Russia. pp. 697-700. Lecture Notes in Computer Science 7814. Springer Verlag. ISSN 0302-9743 ISBN 978-3-642-36972-8
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Official URL: http://dx.doi.org/10.1007/978-3-642-36973-5_63
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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