Snippet-Based relevance predictions for federated web search

Authors:
Thomas Demeester;Dong Nguyen;Dolf Trieschnigg;Chris Develder;Djoerd Hiemstra
Affiliations:
Ghent University - iMinds, Ghent, Belgium;University of Twente, Enschede, The Netherlands;University of Twente, Enschede, The Netherlands;Ghent University - iMinds, Ghent, Belgium;University of Twente, Enschede, The Netherlands
Venue:
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Year:
2013

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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.