trent aptedapc: leveraging... user models13/10/03 slide 1 leveraging... user models leveraging data...

Trent Apted APC: 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


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 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

● 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

● 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

● 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 – 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)

● 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

● 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)


The plots


Experimental Conditions;Results


Findings


Differential Adaptation Revisited


Summary

● Now Dave can rip into it

trent aptedapc: leveraging... user models13/10/03 slide 1 leveraging... user models leveraging data...

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