Paper Info
Title
Surrogate scoring for improved metasearch precision
Abstract
We describe a method for improving the precision of metasearch results based upon scoring the visual features of documents' surrogate representations. These surrogate scores are used during fusion in place of the original scores or ranks provided by the underlying search engines. Visual features are extracted from typical search result surrogate information, such as title, snippet, URL, and rank. This approach specifically avoids the use of search engine-specific scores and collection statistics that are required by most traditional fusion strategies. This restriction correctly reflects the use of metasearch in practice, in which knowledge of the underlying search engines' strategies cannot be assumed. We evaluate our approach using a precision-oriented test collection of manually-constructed binary relevance judgments for the top ten results from ten web search engines over 896 queries. We show that our visual fusion approach significantly outperforms the rCombMNZ fusion algorithm by 5.71%, with 99% confidence, and the best individual web search engine by 10.9%, with 99% confidence.
Year
DOI
Venue
2005
10.1145/1076034.1076139
SIGIR
Keywords
Field
DocType
surrogate scoring,surrogate representation,traditional fusion strategy,underlying search engine,rcombmnz fusion algorithm,typical search result surrogate,search engine-specific score,visual feature,visual fusion approach,individual web search engine,web search engine,improved metasearch precision,machine learning,metasearch,search engine,data fusion
Web search engine,Data mining,Search aggregator,Metasearch engine,Search engine,Information retrieval,Computer science,Sensor fusion,Search analytics,Snippet
Conference
ISBN
Citations 
PageRank 
1-59593-034-5
10
1.78
References 
Authors
6
5
Name
Order
Citations
PageRank
Steven M. Beitzel169646.72
Eric C. Jensen269646.72
Ophir Frieder33300419.55
Abdur Chowdhury42013160.59
Greg Pass5101085.31