Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Evaluating evaluation measure stability
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Condorcet fusion for improved retrieval
Proceedings of the eleventh international conference on Information and knowledge management
Operational requirements for scalable search systems
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Fusion of effective retrieval strategies in the same information retrieval system
Journal of the American Society for Information Science and Technology
A framework for determining necessary query set sizes to evaluate web search effectiveness
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
InfoScale '06 Proceedings of the 1st international conference on Scalable information systems
Improving high accuracy retrieval by eliminating the uneven correlation effect in data fusion
Journal of the American Society for Information Science and Technology
How many high-level concepts will fill the semantic gap in news video retrieval?
Proceedings of the 6th ACM international conference on Image and video retrieval
From "Identical" to "Similar": Fusing Retrieved Lists Based on Inter-document Similarities
ICTIR '09 Proceedings of the 2nd International Conference on Theory of Information Retrieval: Advances in Information Retrieval Theory
Cluster-based fusion of retrieved lists
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Re-ranking search results using an additional retrieved list
Information Retrieval
From "identical" to "similar": fusing retrieved lists based on inter-document similarities
Journal of Artificial Intelligence Research
Assistive tagging: A survey of multimedia tagging with human-computer joint exploration
ACM Computing Surveys (CSUR)
| Hi-index | 0.00 |
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.