1. Learning Profiles from User Interactions
Pelin Atahan and Sumit Sarkar
School of Management,
The University of Texas at Dallas
The University of Texas at Dallas

2. Introduction
Personalization systems tailor content and services to individuals
Consider vendor selling products through its website
Personalize recommendations
Learn profiles based on links visited by a user
user visits a link (l) to which 70% of visitors are male
predict user is male with probability 0.7 and
revise this probability as the user navigates through the website, i.e., clicks on other links
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3. Research Framework Learn profiles for targeting purposes
personal profiles – demographic, psychographic, geographic attributes
predetermined set of attributes, e.g., gender, income, risk taker
Profile representation –attribute values with relevant probabilities
for attribute “gender” (G) profile maybe represented as P(G=m│l)=0.7, and P(G=f │l)=0.3
for attribute “risk taker” (R) with values risk taker (r), conservative (c), P(R=r│l)=0.6, P(R=c│l)=0.4
Probabilistic representation

4. Data Requirements
Data requirements – link level statistics only (for all links)
examples:
P(G=m│”finance” link)=0.7, P(G=f│”finance” link)=0.3
P(R=r│”finance” link)=0.6 and P(R=c│”finance” link)=0.4
Data can be acquired from one of the following sources
registered users, if available
sampling – explicitly asking a subset of users
professional market research agencies like comScore, Claritas, and Nielsen/Net Ratings.

5. Research Problems
Learn the personal profile of a user based on links traversed during a session
Two types of learning considered
Learning profiles passively by observing links traversed
Learning profiles quickly by dynamically determining links available on a page
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6. Literature Review
Primarily study profiling in information retrieval context
user interests
identifying interesting pages based on pages visited.
profiles represented as feature (term) vectors
Montgomery (2001) address learning demographic profiles from websites visited by a user
approach is faulty (conditioning is incorrect)
Baglioni et al. (2003) address identifying the gender of a user based on links visited
consider a subset of pages
apply several classification models
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7. Passive Learning
Consider, Yahoo wants to learn the gender of a user who is traversing its website
user clicks on the following links
the “finance” link (l1)
the “investing ideas” link (l2)
the “insurance” link (l3)
the “sports” link (l4)
problem: To determine the probability that the visitor is male (or female) given this clickstream { l1, l2, l3, l4}
P(G=m│l1’ l2, l3, l4)
In general, for attribute (A) and clickstream { l1, l2, …, ln}
P(A=ai│l1’ l2, …, ln)
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8. Passive Learning Cont’d
Use Bayes formula
where
Assume conditional independence, i.e., probability of clicking a link is independent of the probability of clicking another link, when the user profile is known
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9. Passive Learning Cont’d
Directly estimating P(lj│ai) is difficult
From Bayes formula
We obtain
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10. Passive Learning Cont’d
After algebraic manipulations, we get:
We can learn customer profile from simple link statistics
The process is not computationally intensive
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11. Illustrative Example
Consider the following site priors and link probabilities
P(m│l1’ l2, l3, l4)= 0.91 and P(f│l1’ l2, l3, l4)= 0.09.
site priors
P(m)=0.45
P(f)=0.55
finance link (l1),
P(m│l1)=0.6
P(f│l1)=0.4
investing ideas link (l2),
P(m│l2)=0.7
P(f│l2)=0.3
insurance link (l3),
P(m│l3)=0.4
P(f│l3)=0.6
sports link (l4)
P(m│l4)=0.7
P(f│l4)=0.3
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12. Learning Profiles in Real Time
What happens when the user clicks on a new link?
NBA scoreboard link (l5)
Incremental belief revision
LH – denotes the link history (links clicked prior to the last click)
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13. Incremental Revision Example
P(m│LH, l5)=?
P(m│LH)=P(m│l1’ l2, l3, l4)= 0.91 and P(f│LH)=0.09
P(m│l5)=0.65 and P(f│l5)=0.35
P(m│LH,l5)= 0.96 and P(f│LH, l5)= 0.04
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14. Active Learning of User Profiles
By learning profiles quickly, websites start getting the benefits sooner
Learning is the reduction in uncertainty of profile attributes
Our objective: Learn profiles quickly by carefully selecting the links to offer at each page (offer set)
Information value of an offer set is measured as the expected information gain
The number of links to offer (n) is predetermined
Assume the user will click one of the links available
Stop learning when expected additional information is not statistically significant
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15. Click Probabilities Conditional on an Offer Set
Offer set O={o1,o2,…,on}
We estimate P’(lj│ai) for each attribute value and each link in the offer set.
From Bayes rule:
We need some measure of the likelihood of a link being clicked, P(lj).
does not need to be absolute, a relative measure is sufficient
e.g., number of clicks a link gets per month
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16. Belief Revision Conditional on an Offer Set
Manipulating the above expression we get:
P’(ai│LH) corresponds to the prior on the attribute value at each iteration
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17. Information Gain Given a Link is Clicked
Information gain: Defined as the reduction in entropy of attribute’s distribution given a link is clicked
Entropy prior to a click
Entropy given a link is clicked
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18. Expected Information Gain Given an Offer Set
When n links are offered
P’(lj│LH) is the probability of a link being clicked given the offer set
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19. Optimal Offer Set-One Step Look Ahead
Prior entropy is constant given the link history
We can determine optimal offer set that minimizes the expected entropy
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20. Illustrative Example The user has visited the “finance” link and
There are three possible links to consider
Offer set size n=2
Three possible offer sets: O1={o1, o2}, O2={o1, o3}, O3={o2, o3}.
EI(G│lj, O1)=0.06
EI(G│lj, O2)=0.18
EI(G│lj, O3)=0.04
Offering O2 is optimal
LH=“finance” link
P(m│LH)=0.6
P(f│LH)=0.4
“Investing ideas” link (l1),
P(m│l1)=0.7
P(f│l1)=0.3
P(l1)=0.2
“Insurance” link (l2),
P(m│l2)=0.4
P(f│l2)=0.6
P(l2)=0.3
“Family and home” link (l3),
P(m│l3)=0.2
P(f│l3)=0.8
P(l3)=0.3
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21. Determining the Optimal Offer Set
The number of potential offer sets to evaluate could be very large
For a site with M links and offer set size n, number of possible combinations:
E.g. for M = 100 and n = 10, there are more than 17 trillion combinations
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22. Heuristic Approach to Determine the Optimal Offer Set
Consider the expected entropy expression for learning the gender (n = 2)
P’(lj,ai), is proportional to P(lj,ai), the joint distribution of the aggregate link probabilities
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23. Heuristic Approach to Determine the Optimal Offer Set
To select n links to offer
For each attribute value, select link that maximizes P(ai,lj)
If more links needed, evaluate links with the next highest joint probability
Continue until all n links have been determined.
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24. Discussions
Assumption: the probability of clicking a link is conditionally independent of the probability of clicking other links.
If this assumption does not hold for some links, we can group the correlated links into disjoint sets,
use joint probabilities associated with these groups of links for belief revision, or
use aggregate group level probability parameters to revise beliefs
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25. Discussions
Assumption: the user will follow one of the links being offered.
Other possibilities
the user may leave the site
the user may click the back button, and select a different link
if there is a search engine available on the site, the user may submit a query and navigate to the results page
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26. Conclusion
Presented a framework for modeling user profiles for targeting purposes
Showed how the profile can be learnt implicitly from the links traversed
Showed how the learning process can be expedited by dynamically determining the offer set at each iteration
Data requirements are reasonable
Computationally not intensive
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27. On-going Work
Solution approaches to the optimal offer set selection problem – refine heuristic
Validate the models
Extend the model to learn multiple attributes simultaneously
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28. Thank you!
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