1. 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

2. 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

3. 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

4. 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]

5. 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

6. 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

7. 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

8. 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

10. 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)

11. The plots

12. Experimental Conditions;Results

13. Findings

14. Differential Adaptation Revisited

15. Summary • Now Dave can rip into it