Automatic combination of multiple ranked retrieval systems
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
The effectiveness of GIOSS for the text database discovery problem
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Searching distributed collections with inference networks
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Evaluating and optimizing autonomous text classification systems
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Experiences with selecting search engines using metasearch
ACM Transactions on Information Systems (TOIS)
Comparing the performance of database selection algorithms
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Efficient and effective metasearch for a large number of text databases
Proceedings of the eighth international conference on Information and knowledge management
A unified environment for fusion of information retrieval approaches
Proceedings of the eighth international conference on Information and knowledge management
Towards a highly-scalable and effective metasearch engine
Proceedings of the 10th international conference on World Wide Web
Fusion Via a Linear Combination of Scores
Information Retrieval
Metrics for Evaluating Database Selection Techniques
DEXA '99 Proceedings of the 10th International Workshop on Database & Expert Systems Applications
Towards comprehensive web search
Towards comprehensive web search
Using extra-topical user preferences to improve web-based metasearch
Using extra-topical user preferences to improve web-based metasearch
Intelligent metasearch engine for knowledge management
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Shadow document methods of resutls merging
Proceedings of the 2004 ACM symposium on Applied computing
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
Feature bagging for outlier detection
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Query chains: learning to rank from implicit feedback
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Implementation and evaluation of a quality-based search engine
Proceedings of the seventeenth conference on Hypertext and hypermedia
Improving high accuracy retrieval by eliminating the uneven correlation effect in data fusion
Journal of the American Society for Information Science and Technology
Web searcher interaction with the Dogpile.com metasearch engine
Journal of the American Society for Information Science and Technology
Result merging methods in distributed information retrieval with overlapping databases
Information Retrieval
On exploiting classification taxonomies in recommender systems
AI Communications - Recommender Systems
Robust result merging using sample-based score estimates
ACM Transactions on Information Systems (TOIS)
Effective rank aggregation for metasearching
Journal of Systems and Software
Augmenting image processing with social tag mining for landmark recognition
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part I
Search result diversity for informational queries
Proceedings of the 20th international conference on World wide web
Foundations and Trends in Information Retrieval
Adapting document ranking to users’ preferences using click-through data
AIRS'06 Proceedings of the Third Asia conference on Information Retrieval Technology
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Recent increase in the number of search engines on the Web and the availability of meta search engines that can query multiple search engines makes it important to find effective methods for combining results coming from different sources. In this paper we introduce novel methods for reranking in a meta search environment based on expert agreement and contents of the snippets. We also introduce an objective way of evaluating different methods for ranking search results that is based upon implicit user judgements. We incorporated our methods and two variations of commonly used merging methods in our meta search engine, Mearf, and carried out an experimental study using logs accumulated over a period of twelve months. Our experiments show that the choice of the method used for merging the output produced by different search engines plays a significant role in the overall quality of the search results. In almost all cases examined, results produced by some of the new methods introduced were consistently better than the ones produced by traditional methods commonly used in various meta search engines. These observations suggest that the proposed methods can offer a relatively inexpensive way of improving the meta search experience over existing methods.