Agglomerative clustering of a search engine query log
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Relevance score normalization for metasearch
Proceedings of the tenth international conference on Information and knowledge management
Query clustering using user logs
ACM Transactions on Information Systems (TOIS)
Condorcet fusion for improved retrieval
Proceedings of the eleventh international conference on Information and knowledge management
Cranking: Combining Rankings Using Conditional Probability Models on Permutations
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Query Expansion by Mining User Logs
IEEE Transactions on Knowledge and Data Engineering
Web metasearch: rank vs. score based rank aggregation methods
Proceedings of the 2003 ACM symposium on Applied computing
ACM Transactions on Internet Technology (TOIT)
Context-sensitive information retrieval using implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Query expansion using random walk models
Proceedings of the 14th ACM international conference on Information and knowledge management
Mining search engine query logs for query recommendation
Proceedings of the 15th international conference on World Wide Web
Mining dependency relations for query expansion in passage retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Personalized query expansion for the web
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
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
Improving search engines by query clustering
Journal of the American Society for Information Science and Technology
Modeling anchor text and classifying queries to enhance web document retrieval
Proceedings of the 17th international conference on World Wide Web
Introduction to Information Retrieval
Introduction to Information Retrieval
Query-URL bipartite based approach to personalized query recommendation
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Efficient querying relaxed dominant relationship between product items based on rank aggregation
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Segmentation of search engine results for effective data-fusion
ECIR'07 Proceedings of the 29th European conference on IR research
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Currently, commercial search engines have implemented methods to suggest alternative Web queries to users, which helps them specify alternative related queries in pursuit of finding needed Web pages. In this paper, we address the Web search problem on related queries to improve retrieval quality by devising a novel search rank aggregation mechanism. Given an initial query and the suggested related queries, our search system concurrently processes their search result lists from an existing search engine and then forms a single list aggregated by all the retrieved lists. In particular we propose a generic rank aggregation framework which considers not only the number of wins that an item won in a competition, but also the quality of its competitor items in calculating the ranking of Web items. The framework combines the traditional and random walk based rank aggregation methods to produce a more reasonable list to users. Experimental results show that the proposed approach can clearly improve the retrieval quality in a parallel manner over the traditional search strategy that serially returns result lists. Moreover, we also empirically investigate how different rank aggregation methods affect the retrieval performance.