1. Trent AptedAPC: Leveraging... User Models13/10/03 Slide 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. Trent AptedAPC: Leveraging... User Models13/10/03 Slide 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. Trent AptedAPC: Leveraging... User Models13/10/03 Slide 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. Trent AptedAPC: Leveraging... User Models13/10/03 Slide 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. Trent AptedAPC: Leveraging... User Models13/10/03 Slide 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. Trent AptedAPC: Leveraging... User Models13/10/03 Slide 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 A H UGIN method (next slide) ● Model #4 - Individual Model – Learned entirely on individual data

7. Trent AptedAPC: Leveraging... User Models13/10/03 Slide 7 A Tangent – A H UGIN [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. Trent AptedAPC: Leveraging... User Models13/10/03 Slide 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

9. Trent AptedAPC: Leveraging... User Models13/10/03 Slide 9

10. Trent AptedAPC: Leveraging... User Models13/10/03 Slide 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. Trent AptedAPC: Leveraging... User Models13/10/03 Slide 11 The plots

12. Trent AptedAPC: Leveraging... User Models13/10/03 Slide 12 Experimental Conditions; Results

13. Trent AptedAPC: Leveraging... User Models13/10/03 Slide 13 Findings

14. Trent AptedAPC: Leveraging... User Models13/10/03 Slide 14 Differential Adaptation Revisited

15. Trent AptedAPC: Leveraging... User Models13/10/03 Slide 15 Summary ● Now Dave can rip into it

16. Trent AptedAPC: Leveraging... User Models13/10/03 Slide 16

17. Trent AptedAPC: Leveraging... User Models13/10/03 Slide 17