Evaluation of an inference network-based retrieval model
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Silk from a sow's ear: extracting usable structures from the Web
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Computational models of information scent-following in a very large browsable text collection
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Visualizing the evolution of Web ecologies
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Improved algorithms for topic distillation in a hyperlinked environment
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Automatic resource compilation by analyzing hyperlink structure and associated text
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A collaborative foraging approach to web browsing enrichment
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Predicting category accesses for a user in a structured information space
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
The catacomb project: building a user-centered portal the conversational way
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The effects of information scent on visual search in the hyperbolic tree browser
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ScentTrails: Integrating browsing and searching on the Web
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How users assess web pages for information seeking
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Mining interesting knowledge from weblogs: a survey
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Model-directed Web transactions under constrained modalities
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Using information scent to model the dynamic foraging behavior of programmers in maintenance tasks
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Improving website search with server log analysis and multiple evidence combination
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A hybrid web recommender system based on Q-learning
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How do users find things with PubMed?: towards automatic utility evaluation with user simulations
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Modeling information navigation: implications for information architecture
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PathRank: Web Page Retrieval with Navigation Path
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Predicting strong associations on the basis of corpus data
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Looking Ahead: A Comparison of Page Preview Techniques for Goal-Directed Web Navigation
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Personalised web search using ACO with information scent
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On the Web, users typically forage for information by navigating from page to page along Web links. Their surfing patterns or actions are guided by their information needs. Researchers need tools to explore the complex interactions between user needs, user actions, and the structures and contents of the Web. In this paper, we describe two computational methods for understanding the relationship between user needs and user actions. First, for a particular pattern of surfing, we seek to infer the associated information need. Second, given an information need, and some pages as starting pints, we attempt to predict the expected surfing patterns. The algorithms use a concept called “information scent”, which is the subjective sense of value and cost of accessing a page based on perceptual cues. We present an empirical evaluation of these two algorithms, and show their effectiveness.