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Relevance and economic feedback to learn user preferences may be used to produce an ordering of documents that supports browsing in hypermedia, adaptive hypertext, and digital libraries. Document classification based on the reflected Gray code provides paths through the entire collection, each path traversing each node in the set of documents exactly once. Systems organizing documents based on weighted and unweighted Gray codes are examined. Relevance feedback is used to conceptually organize the collection for an individual to browse, based on that individual's interests and information needs, as reflected by their relevance judgements and user supplied economic preferences. We apply Bayesian learning theory to estimating the characteristics of documents of interest to the user and supply an analytic model of browsing performance, based on minimizing the Expected Browsing Distance (EBD). Economic feedback may be used to change the ordering of documents to benefit the user. Using these techniques, a hypermedia or digital library may order any and all available documents, not just those examined, based on the information provided by the searcher or people with similar interests.
One can similarly browse through tables of data.
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