Loglinear and Multidimensional Scaling Models of Webbased Information Spaces

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Loglinear and Multidimensional Scaling Models of
Web-based Information Spaces

• René F. Reitsma,
• Schwartz School of Business and Information
  Systems
• St. Francis Xavier University
• Antigonish, Nova Scotia
• Barbara P. Buttenfield
• Dept. of GeographyUniversity of Colorado, Boulder

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Loglinear and Multidimensional Scaling Models of
Web-based Information Spaces

• Cartography of the Internet
• Alexandria Digital Libray (ADL)
• Research questions
• Modeling ADL transactions
• Finding stable patterns loglinear models
• Maps from traffic data multidimensional scaling
• Interpretation of results
• Discussion and new work

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Cartography of the Internet

• Is there such a thing as information/cyber space?
• We navigate it so.
• Does it have dimensions?
• How many?
• Is it orthogonal?
• What is its metric (how is distance measured?)
• Can we map it?

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Alexandria Digital Library (ADL)http//www.alexan
dria.ucsb.edu

• Comprehensive library services for
• acquisition
• cataloging
• browsing
• retrieval
• collection maintenance
• distributed data archives of
• library components spread across the Internet
• delivery on wide-area networks
• geographically referenced information

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Alexandria Digital Library (Cont.d)

• Holdings include
• Subset of UCSB Map and Imagery Library (gt
  2,000,000)
• Additional data sets coming on-line
• Functions
• Browsing and retrieving maps and imagery
• Spatial data sets, catalog and gazetteer
• Tutorials, on-line help and general reference
  information

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Research Questions

• Can we conceptualize ADL (and other web sites) as
  an information space?
• If so, what does that space look like? Can we
  (re)construct this space?
• Can we use ADL transactions to reconstruct that
  space?
• Do transaction patterns (and hence the ADL
  'space') change over time e.g., as a consequence
  of changes to the user-interface?
• Do different user groups navigate different
  spaces? Why?

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ADL 'Rooms'
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ADL Transactions
((User X, Room P, Time T) , (User X, Room Q, Time
Ti))

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Loglinear Models of ADL Transactions

• Transaction matrix as contingency table

• Spatial interaction Willekens (1983), Willekens
  and Baydar (1983, 1983a), Aufhauser Fischer
  (1985)
• Social mobility DiPrete (1990), Miller and Hayes
  (1990)
• Science citation Everett and Pecotich (1991)

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Loglinear Modeling (Concept)

• ?2 analysis
• - Expected Fij P(row i ? column j) F
• Fi/F Fj/F F
• - Observed Fij Expected Fij Interaction

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Loglinear Modeling (Cont.d)

• ?OD as statictically stable indicator of traffic
  intensity

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Loglinear Models of ADL Transactions(Cont.d)

• Do transaction patterns change over time?

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ADL Loglinear Results (All users, n175,606)
 
 
 



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Spatial Interpretation of Transactions

• Don't know the space's dimensionality nor its
  metric.
• Don't know the location of the pages/rooms in the
  space.
• Assumption distance is inversely related to
  interaction.
• Multidimensional scaling

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?OD as Distance Estimate
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MDS Scaling results
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Dimensional Interpretation

• Dimension 1 user- vs. system directed
• Dimension 2 topical vs. administrative
• Dimension 3 actions vs. learning
• Dimension 4 time on task

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Research Questions

• Can we (re)construct information space?
• Consider the statistically stable transactions as
  'trips' or 'moves' made trough n-dimensional
  space.
• Apply MDS based on ?OD - distance assumption.
• Does the space (transaction patterns) change over
  time e.g., as a consequence of changes to the
  user-interface?
• - not significant

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Research Questions (Cont.'d)

• Do different user groups navigate different
  spaces?

- Librarians (untrained) - Panelists
(trained)
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Discussion

• So far inductive ? deductive follow up
• Current transactions ? stable patterns ? theory
• Needed theory ? predict patterns ? testing
• LLM implies averaging important signatures can
  get lost in surrounding noise
• From pattern to path.
• Room 'extensions' from points to n-dimensional
  volumes.

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ADL vs. Most Web Sites

• ADL transaction
• ((User X, Page P, Time T),(User X, Page Q, Time
  Ti))
• ADL had definite entry point sessions could be
  uniquely identified.
• Most web sites no entry point ? no constraint on
  i.
• ((User X, Page P, Mon 900 AM),(User X, Page Q,
  Wed 400 PM))
• ???
• Need correction.

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Server Log Correction

• Assumption The less two consecutive visits are
  separated in time, the more likely they are to
  represent a transaction.
• P(transaction) f(time)
• Problem How to estimate f ?
• Proposal Estimate f empirically
• For each candidate transaction i,j, record its
  duration.
• For all candidate transactions i,j, fit a
  probability density function.
• Weigh each candidate transaction I,j, with its
  probability.