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Leveraging ... User Models. Leveraging Data About Users in General in the Learning of Individual User Models* Anthony Jameson PhD (Psychology) Adjunct Professor of HCI Frank Wittig CS Researcher Saarland University, Saarbrucken Germany * i.e. pooling knowledge to improve learning accuracy.

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leveraging user models
Leveraging ... User Models

Leveraging Data About Users in General in the Learning of Individual User Models*

  • Anthony Jameson PhD (Psychology)
    • Adjunct Professor of HCI
  • Frank Wittig
    • CS Researcher
  • Saarland University, Saarbrucken Germany

*i.e. pooling knowledge to improve learning accuracy

their contributions
Their Contributions
  • Answer the question:
    • How can systems that employ Bayesian networks to model users most effectively exploit data about users in general and data about the individual user?
  • Most previous approaches looked only at:
    • Learning general user models
      • Apply the model to users in general
    • Learning individual user models
      • Apply each model to its particular user
collaborative filtering and bayesian networks
Collaborative Filtering and Bayesian Networks
  • Collaborative filtering systems can make individualised predictions based on a subset of users determined to be similar to U
  • But sometimes we want a more interpretable model 
    • Causal relationships are represented explicitly
    • Can predict behaviour of U based on contextual factors
    • Can make inferences about unobserved contextual factors
  • Bayesian networks are more straightforwardly applied to this type of task
collaborative filtering example recommending products
Collaborative Filtering Example – Recommending Products
  • Each user rates a subset of products
    • Determines the users tastes as well as product quality
  • To recommend a CD for user U
    • First look for users especially similar to U
      • ie who have rated similar items in a similar way
    • Compute the average rating for this subset of users
    • Recommend products with high ratings
  • Used by Amazon.com, CDNow.com and MovieFinder.com [Herlocker et al. 1999]
their experiment inferring psychological states of the user
Their Experiment - Inferring Psychological States of the User
  • Simulated on a computer workstation
  • Navigating through a crowded airport while asking a mobile assistant questions via speech
  • Pictures appeared to prompt questions
    • Some instructed time pressure
      • Finish each utterance as quickly as possible
    • Some instructed to do a secondary task
      • “navigate” through terminal (using arrow keys)
  • Speech input was later coded semi-automatically to extract features
learning models used
Learning Models Used
  • Model #1 - General Model
    • Learned from experimental data via maximum-likelihood method (not adapted to individual users)
  • Model #2 - Parametrised Model
    • Like general model, but baselines for each user and for each speech metric are included
  • Model #3 - Adaptive (Differential) Model
    • Uses AHUGIN method (next slide)
  • Model #4 - Individual Model
    • Learned entirely on individual data
a tangent a h ugin olesen et al 1992
A Tangent – AHUGIN[Olesen et al. 1992]
  • Adaptive HUGIN
  • No explicit dimensional representation for how users differ
  • The conditional probability tables (CPTs) of the Bayesian network are adapted with each observation
  • Thus a variety of individual differences can be adapted to, without the designer of the BN anticipating their nature
equivalent sample size ess
Equivalent Sample Size (ESS)
  • However, you also need to address the speed at which the CPTs adapt
  • The ESS represents the extent of the system's reliance on the initial general model, relative to each users' new data
  • This paper contributes a principled method of estimating the optimal ESS, which is generally not obvious a priori, nor consistent across the parts of the BN
  • Differential adaptation
speech metrics results
Speech Metrics;Results
  • Articulation Rate
    • Syllables articulated per second of speaking
    • General performs worst, other three on par
      • Individual takes a while to catch up, as with all metrics
  • Number of Syllables
    • The number of syllables in the utterance
    • Again, General is poor, Parametrised OK, Individual and Adaptive best
  • Disfluencies and Silent Pauses
    • Any of four types of disfluency; eg failing to complete a sentence
    • Duration of silent pauses relative to word number
    • All about equal (perhaps due to infrequencies)
  • Now Dave can rip into it