Combining multiple evidence from different properties of weighting schemes
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Journal of the American Society for Information Science
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International Journal of Computer Vision
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Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
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Information Processing and Management: an International Journal
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Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
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Journal of the American Society for Information Science and Technology
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Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
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ACM Transactions on Information Systems (TOIS)
Visualizing Incomplete and Partially Ranked Data
IEEE Transactions on Visualization and Computer Graphics
On rank correlation and the distance between rankings
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Understanding the privacy-personalization dilemma for web search: a user perspective
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Measuring personalization of web search
Proceedings of the 22nd international conference on World Wide Web
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We introduce a new dissimilarity function for ranked lists, the expected weighted Hoeffding distance, that has several advantages over current dissimilarity measures for ranked search results. First, it is easily customized for users who pay varying degrees of attention to websites at different ranks. Second, unlike existing measures such as generalized Kendall's tau, it is based on a true metric, preserving meaningful embeddings when visualization techniques like multi-dimensional scaling are applied. Third, our measure can effectively handle partial or missing rank information while retaining a probabilistic interpretation. Finally, the measure can be made computationally tractable and we give a highly efficient algorithm for computing it. We then apply our new metric with multi-dimensional scaling to visualize and explore relationships between the result sets from different search engines, showing how the weighted Hoeffding distance can distinguish important differences in search engine behavior that are not apparent with other rank-distance metrics. Such visualizations are highly effective at summarizing and analyzing insights on which search engines to use, what search strategies users can employ, and how search results evolve over time. We demonstrate our techniques using a collection of popular search engines, a representative set of queries, and frequently used query manipulation methods.