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Bayesian Adaptive User

Profiling with Explicit & Implicit

Feedback

Philip Zigoris, Yi ZhangUniversity of California, Santa Cruz

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Obstacles to Personalization

1. Asking for feedback is intrusive.

Implicit Feedback - Infer user feedback

from user behavior.

Little to no information about new

users (the cold-start problem).Borrow information from existing users

via Bayesian hierarchical model.

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Outline

Implicit Feedback

Hierarchical Bayesian Framework

Gaussian Network

Experiments

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Implicit Feedback

A users interaction with a document suggests

their opinion of it.

Includes: Keyboard usage Mouse usage Viewing time Eye-tracking

Appeal: cheap! **Modulo security issues

Question: Does it have any predictive value?

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Is Implicit Feedback Useful?

Yes Positive correlation between the time spent

viewing a page and a users opinion of thepage. [Claypool et al., 2001; Fox et al.,2005]

No (sometimes)

In Kelly et al., 2004 demonstrates thatcorrelation varies significantly across tasks.

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Outline

Implicit Feedback

Hierarchical Bayesian Framework

Gaussian Network

Experiments

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f

I Like!

X1 Y1

X2 Y2

X3 Y3

The Task at Hand

Doc Rating

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Cold-Start Problem

Personalized systems require training

data.

Users do not want to endure poorperformance while the system is

learning.

Solution: Give the system a head-start with some (Bayesian) prior

knowledge.

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Bayesian Prior over User Profiles

User-ModelSpace

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fu ~ P(f |q)

y ~ fu(x)

Parameter describing

prior distribution

Hierarchical User ModelGeneric Form

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P( fu |q)P( fu |q,(x1,y1))P( fu |q,(x1,y1),(x2,y2 ))P( fu |q,(x1,y1),K ,(x3,y3 ))P( fu |q,(x1,y1),K ,(x4,y4 ))P( fu |q,(x1,y1),K ,(x5,y5 ))

Refining Beliefs Based on User Data

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fMAPu

= argmaxf

P(f |q)P(Du

| f)[ ]

= argmaf

x log(P(f |q)) + log(P(Du

| f))[ ]

The Posterior Distribution

P( fu |q,Du) =P(D

u| f

u,q)P( f

u|q)

P(Du

|q)

Influence of

prior

Data

likelihood

Maximum A Posteriorimodel:

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Tradeoff: Prior vs. Data

More data data likelihood term will

dominate the objective function.

log(P(Du | f)) = log P(f(x i) = y i)( )

= log(P(f(x i) = y i))

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Outline

Implicit Feedback

Hierarchical Bayesian Framework

Gaussian Network

Experiments

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Gaussian Network

wu ~ N(m,S)

y ~ N(xT wu,ku2)

wMAPu = argmin

w

(w - m)TS- 1(w - m)+ 1ku

(x iT w - y i)

2

i

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Learning the Prior from

Existing Users

Unbiased estimator for prior parameters:

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Outline

Implicit Feedback

Hierarchical Bayesian Framework

Gaussian Network

Experiments

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Experimental Datasets

Claypool

75 Student Volunteers, 1823 DocumentsUnguided, unrestricted web browsing for 20-30 minutes

Every document explicitly rated (Scale 1 to 5)

Zhang

15 Users, 4663 Documents

1 month, users spent >1 hour everyday using system Focused on reading news articlesNot every document is explicitly rated (Scale 1 to 5)Includes other forms of explicit feedback (TBA)

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Feature Sets

Document length

Speed of host server

Number of pages linking to host server

Document

Mouse usage

Keyboard usage

Time spent on page

Implicit

Relevance Score

Readability ScoreExplicit(Zhang)

ExamplesType

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Evaluation Methodology

Tested four models

Linear model with (P)rior

Linear model with (N)o prior

(G)eneric, user-independent model

Moving (A)verage

Original ordering preserved

Leave-one-user-out Model updated and evaluated (error2) after

every example

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Summary of Results

Performance (MSE) averaged over time and user.

Hierarchical model significantly*

outperformsother methods.

* (95% Wilcoxon signed rank test)

Explicit

ImplicitImplicit &

Explicit

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Performance Over TimeExplicit & Implicit Feedback

Shifting rating bias

Personalization seems

to begin

Prior getting

in the way

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Performance Over TimeImplicit Feedback Only (Zhang et al)

Follows moving

average

Implicit feedback

becomes useful

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Performance Over TimeImplicit Feedback Only (Claypool et al)

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Comparative Performance

with Different Feature Sets

Implicit feedback

hurts performance

Implicit feedbackbenefits performance

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Discussion: The Dynamics of the

User Model

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Discussion: Is Implicit Feedback

Useful? Why?

Error can be decomposed into:

Bias - How closely the learning algorithm can approximate the bestsolution.

Variance - Sensitivity of learning algorithm to the training sample.

Noise - Irreducible uncertainty of problem

Including implicit feedback increases variance. This explains why:Negative impact on early performanceRequires substantial amount of training data to be useful.

Linear model has strong bias. This may explain why:Implicit feedback does not perform well on its own.

Di i Shif i U

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Discussion: Shifting User

Behavior

Implicit assumption of our model:

User behavior is consistent over time.

Already observed shifting bias in rating.

Also, observed shifts in keyboard andmouse usage.

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Conclusion

Implicit feedback (of the type used in our study) has

marginal predictive value.

A question ofwhen, not if

Requires personalization

Requires lots of data

Hierarchical model effectively alleviates cold-

start problem

Shifting user behavior exists

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Thank You!