Probabilistic and genetic algorithms in document retrieval
Communications of the ACM
SIGIR '89 Proceedings of the 12th annual international ACM SIGIR conference on Research and development in information retrieval
A neural network for probabilistic information retrieval
SIGIR '89 Proceedings of the 12th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
A probabilistic learning approach for document indexing
ACM Transactions on Information Systems (TOIS) - Special issue on research and development in information retrieval
Journal of the American Society for Information Science
Pivoted document length normalization
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Exploring the similarity space
ACM SIGIR Forum
On Relevance, Probabilistic Indexing and Information Retrieval
Journal of the ACM (JACM)
Evaluating evaluation measure stability
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Agglomerative clustering of a search engine query log
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Improved retrieval effectiveness through impact transformation
ADC '02 Proceedings of the 13th Australasian database conference - Volume 5
Implementation of the SMART Information Retrieval System
Implementation of the SMART Information Retrieval System
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Query chains: learning to rank from implicit feedback
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Using annotations in enterprise search
Proceedings of the 15th international conference on World Wide Web
Community-based snippet-indexes for pseudo-anonymous personalization in web search
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Capturing community search expertise for personalized web search using snippet-indexes
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search
ACM Transactions on Information Systems (TOIS)
Enhancing Case-Based, Collaborative Web Search
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Minimally invasive randomization for collecting unbiased preferences from clickthrough logs
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Query assistant based on experience capitalization for information retrieval systems
HSI'09 Proceedings of the 2nd conference on Human System Interactions
Web Semantics: Science, Services and Agents on the World Wide Web
Using clicks as implicit judgments: expectations versus observations
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Investigating the effectiveness of clickthrough data for document reordering
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Partial-update dimensionality reduction for accumulating co-occurrence events
Pattern Recognition Letters
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This paper considers how web search engines can learn from the successful searches recorded in their user logs.Document Transformation is a feasible approach that uses these logs to improve document representations. Existing test collections do not allow an adequate investigation of Document Transformation, but we show how a rigorous evaluation of this method can be carried out using the referer logs kept by web servers. We also describe a new strategy for Document Transformation that is suitable for long-term incremental learning.Our experiments show that Document Transformation improves retrieval performance over a medium sized collection of webpages.Commercial search engines may be able to achieve similar improvements by incorporating this approach.