Query routing for Web search engines: architectures and experiments
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Building efficient and effective metasearch engines
ACM Computing Surveys (CSUR)
Expert agreement and content based reranking in a meta search environment using Mearf
Proceedings of the 11th international conference on World Wide Web
Fusion Via a Linear Combination of Scores
Information Retrieval
Efficient similarity search and classification via rank aggregation
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Adaptive combination of evidence for information retrieval
Adaptive combination of evidence for information retrieval
Web metasearch: rank vs. score based rank aggregation methods
Proceedings of the 2003 ACM symposium on Applied computing
Ordering patterns by combining opinions from multiple sources
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Aggregating inconsistent information: ranking and clustering
Proceedings of the thirty-seventh annual ACM symposium on Theory of computing
Ordering by weighted number of wins gives a good ranking for weighted tournaments
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Overlap Among Major Web Search Engines
ITNG '06 Proceedings of the Third International Conference on Information Technology: New Generations
Proceedings of the 16th international conference on World Wide Web
An outranking approach for rank aggregation in information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Aggregation of partial rankings, p-ratings and top-m lists
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Segmentation of search engine results for effective data-fusion
ECIR'07 Proceedings of the 29th European conference on IR research
Field-weighted XML retrieval based on BM25
INEX'05 Proceedings of the 4th international conference on Initiative for the Evaluation of XML Retrieval
Feature selection for ordinal text classification
Neural Computation
| Hi-index | 0.00 |
Nowadays, mashup services and especially metasearch engines play an increasingly important role on the Web. Most of users use them directly or indirectly to access and aggregate information from more than one data sources. Similarly to the rest of the search systems, the effectiveness of a metasearch engine is mainly determined by the quality of the results it returns in response to user queries. Since these services do not maintain their own document index, they exploit multiple search engines using a rank aggregation method in order to classify the collected results. However, the rank aggregation methods which have been proposed until now, utilize a very limited set of parameters regarding these results, such as the total number of the exploited resources and the rankings they receive from each individual resource. In this paper we present QuadRank, a new rank aggregation method, which takes into consideration additional information regarding the query terms, the collected results and the data correlated to each of these results (title, textual snippet, URL, individual ranking and others). We have implemented and tested QuadRank in a real-world metasearch engine, QuadSearch, a system developed as a testbed for algorithms related to the wide problem of metasearching. The name QuadSearch is related to the current number of the exploited engines (four). We have exhaustively tested QuadRank for both effectiveness and efficiency in the real-world search environment of QuadSearch and also, using a task from the recent TREC-2009 conference. The results we present in our experiments reveal that in most cases QuadRank outperformed all component engines, another metasearch engine (Dogpile) and two successful rank aggregation methods, Borda Count and the Outranking Approach.