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A Hypertext Metric Based on Huffman Coding

A Hypertext Metric Based on Huffman Coding. Chris Coulston Theresa M. Vitolo Penn State Erie Gannon University Electrical and Computer Engineering Computer and Information Science. Motivation. Relationship between Foraging navigation patterns Outcomes measures Metric Correlate

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A Hypertext Metric Based on Huffman Coding

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  1. A Hypertext Metric Based on Huffman Coding Chris Coulston Theresa M. Vitolo Penn State Erie Gannon University Electrical and Computer Engineering Computer and Information Science

  2. Motivation • Relationship between • Foraging navigation patterns • Outcomes measures • Metric • Correlate • Semantics

  3. Prior Work • Botafogo, Rivlin and Shneiderman • Compactness and Stratum • Pirolli, Pitkow, Rao • High level regions of interest (Xerox web site) • McEneaney • Establishes relationship • Small range of metric values • Semantics of metric

  4. Huffman Code • Given: Fixed message • Symbols • Frequencies • Find: Binary encoding of symbols • Minimize total number of bits in message • Huffman tree • Bits per symbol

  5. Example • Message • a,a,a,a,a,a,b,b,b,c,c,d • Huffman Tree

  6. Transformation • User behavior viewed as decoding process • Input • HT topology • User path / Node and link frequencies • Output • Bits per symbol • Binary decisions to get to information in the context of the entire hypertext

  7. Example • HT topology

  8. Example • User path

  9. Example • User Path • BFS • Frequency

  10. Example • User • 3.82 BPS

  11. Example • Optimum • 2.89 BPS

  12. Example • Ratio R=2.89/3.82 = 0.78 • R in (0,1] • R=1 optimal navigation • R a0 inefficient navigation

  13. Conclusions/Future Work • Semantic basis for metric • Analyze McEneaney data • Create software tools • Correlate user success with Huffman metric • Framework for “hunting” • Collaboration with McEneaney • Hypertext ’02

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