Natural language vs. Boolean query evaluation: a comparison of retrieval performance
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Searching the Web: the public and their queries
Journal of the American Society for Information Science and Technology
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Query expansion using associated queries
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Optimizing web search using web click-through data
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Improving web search ranking by incorporating user behavior information
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Mining the search trails of surfing crowds: identifying relevant websites from user activity
Proceedings of the 17th international conference on World Wide Web
Are click-through data adequate for learning web search rankings?
Proceedings of the 17th ACM conference on Information and knowledge management
Investigating external corpus and clickthrough statistics for query expansion in the legal domain
Proceedings of the 17th ACM conference on Information and knowledge management
Translating queries into snippets for improved query expansion
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
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We investigate the task of re-ranking search results based on query log information. Prior work has considered this problem as either the task of learning document rankings of using features based on user behavior, or as the task of enhancing documents and queries using log data. Our contribution combines both. We distill log information into event-centric surrogate documents (ESDs), and extract features from these ESDs to be used in a learned ranking function. Our experiments on a legal corpus demonstrate that features engineered on surrogate documents lead to improved rankings, in particular when the original ranking is of poor quality.