The document discusses the use of contextual Markov models for predicting user intent in web analytics. It covers user intent modeling, the importance of context in understanding user behavior, and presents results from a case study demonstrating the effectiveness of contextual modeling over global approaches. Future research directions include optimizing context discovery and integrating change detection mechanisms.

1. Predicting Current User Intent
with Contextual Markov Models
Julia Kiseleva, Hoang Thanh Lam, Mykola Pechenizkiy (TU/e)
Toon Calders (ULB)
DDDM@ICDM2013,
Dallas, TX, USA
CAPA project: http://www.win.tue.nl/~mpechen/projects/capa/
7 December 2013

2. Outline
• What is predictive Web analytics
• Context-Aware Predictive Analytics framework
• User intent modeling
• Contextual Markov Models
• Case study, experimental results
• Conclusions and further ongoing work
DDDM@ICDM2013
Dec 7, 2013
1Predicting Current User Intent with Contextual Markov Models
Mykola Pechenizkiy, Eindhoven University of Technology

3. Understanding user needs
DDDM@ICDM2013
Dec 7, 2013
2Predicting Current User Intent with Contextual Markov Models
Mykola Pechenizkiy, Eindhoven University of Technology

4. Let’s give it a try…
DDDM@ICDM2013
Dec 7, 2013
3Predicting Current User Intent with Contextual Markov Models
Mykola Pechenizkiy, Eindhoven University of Technology

5. User Intent Modeling: What?
• Next action prediction
– Click prediction in display advertising
– Drop out prediction
– Trail prediction
• Information need prediction:
– Navigational vs. explorative vs. purchase
– Open acronym based on context
• Type of product wanted
– Personalization based on context
DDDM@ICDM2013
Dec 7, 2013
4Predicting Current User Intent with Contextual Markov Models
Mykola Pechenizkiy, Eindhoven University of Technology

6. User Intent Modeling: Why?
• To understand users and website usage
– redesign website, redirect flows,
– diversified search, recommendations
• To better use budget (pageviews)
– what (type of) ads to serve?
– brand awareness CPM, or convergence CPC
• To manipulate user – worth giving a promotion?
– personalize with intent of converging to a desired
action
– personalized suggestions based on user context
DDDM@ICDM2013
Dec 7, 2013
5Predicting Current User Intent with Contextual Markov Models
Mykola Pechenizkiy, Eindhoven University of Technology

7. User Intent Modeling: How?
Model L
population
(source)
Historical
data
labels
label?
1. training
2.
2. application
X
y
X'
y'
Training:
y = L (X)
Application:
use L
for an unseen data
y' = L (X')
labels
Testing
data
DDDM@ICDM2013
Dec 7, 2013
6Predicting Current User Intent with Contextual Markov Models
Mykola Pechenizkiy, Eindhoven University of Technology

8. Context in IR & RecSys
• User Context
– Preferences, usage history, profiles
• Document/Product Context
– Meta-data, content features
• Task Context
– Current activity, location etc.
• Social Context
– Leveraging the social graph
DDDM@ICDM2013
Dec 7, 2013
7Predicting Current User Intent with Contextual Markov Models
Mykola Pechenizkiy, Eindhoven University of Technology

9. Context in Diagnostics
Not predictive alone but a subset of features with the
contextual attribute(s) becomes (much) more predictive
Time of
the day
context
no context
DDDM@ICDM2013
Dec 7, 2013
8Predicting Current User Intent with Contextual Markov Models
Mykola Pechenizkiy, Eindhoven University of Technology

10. Context in Marketing
P(Purchase|gender=“male”)=P(Purchase|gender=“female”)
ModelMale~f(relevance); ModelFemale~f(perceived value)
gender
context
no context
Male
Female
buy
buy
relevance
relevance
buy
don’t
don’t
don’t
gender
DDDM@ICDM2013
Dec 7, 2013
9Predicting Current User Intent with Contextual Markov Models
Mykola Pechenizkiy, Eindhoven University of Technology

11. Environment/
Context
Model L
population
Training:
??
Application:
y' = Lj (X')
Lj <= G(X',E)
X'
y'
Historical
data
labels
X
y
label?
Context-Awareness as Meta-learning
labels
Test
data
DDDM@ICDM2013
Dec 7, 2013
10Predicting Current User Intent with Contextual Markov Models
Mykola Pechenizkiy, Eindhoven University of Technology

12. Learning Classifiers & Context
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Dec 7, 2013
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Mykola Pechenizkiy, Eindhoven University of Technology

13. Research Questions
• How to define the context (form and maintain contextual
categories) in web analytics?
• How to connect context with the prediction process in
predictive web analytics?
• How to integrate change detection mechanisms into the
prediction process in web analytics?
• How to ensure integration and feedback mechanisms
between change detection and context awareness
mechanisms?
• What should a reference architecture allowing to plug in
new context aware prediction techniques for a collection
of web analytics tasks look like?
DDDM@ICDM2013
Dec 7, 2013
13Predicting Current User Intent with Contextual Markov Models
Mykola Pechenizkiy, Eindhoven University of Technology

14. IEEE CBMS 2010
Perth, Australia
Handling Concept Drift in Medical Applications: Importance, Challenges and Solutions
© M. Pechenizkiy and I. Zliobaite
14
• Context-aware
ranking of
search results
• Drop-out
prediction/pre
vention
• Next action
prediction

15. Mastersportal.eu - Homepage
Quick
Search
Banner
Click
Universities
in the
spotlight

16. Mastersportal.eu - Search
Refine
Search
Click on Program
is Search Result
Click on
University
Click on
Country

17. User Navigation Graph

18. Motivation for Contextual Markov Models
Useful Contexts:
E[M] < pc1*E[Mc1] + pc2*E[Mc2]
Why should it help?
Explicit contexts (user location)
Implicit contexts (inferred from clickstream)

19. Implicit Context
Discover clusters in
the graph using
community
detection
algorithm
c1 =
Novice
users
c1 =
Experienced
users
C = user type
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Dec 7, 2013
19Predicting Current User Intent with Contextual Markov Models
Mykola Pechenizkiy, Eindhoven University of Technology

20. Dataset
Date
Source of
information
May 2012
Mastersportal.eu
#sessions 350.618
#requests 1.775.711
DDDM@ICDM2013
Dec 7, 2013
20Predicting Current User Intent with Contextual Markov Models
Mykola Pechenizkiy, Eindhoven University of Technology
Publicly available at:
http://www.win.tue.nl/~mpechen/projects/capa

21. Accuracy Results
DDDM@ICDM2013
Dec 7, 2013
21Predicting Current User Intent with Contextual Markov Models
Mykola Pechenizkiy, Eindhoven University of Technology
user location
user type

22. Global vs. explicit vs. implicit vs. random contexts
DDDM@ICDM2013
Dec 7, 2013
22Predicting Current User Intent with Contextual Markov Models
Mykola Pechenizkiy, Eindhoven University of Technology

23. Conclusions
• We formulated context discovery as
optimization problem
• Our approach can be used to identify
useful contexts
• Experiments on a real dataset provide empirical
evidence that contextual Markov Models are more
accurate than global models
• Further (ongoing) work
– Temporal context discovery (TempWeb@WWW’2013)
– Multidimensional vertical and horizontal clustering on
the user navigation graph
DDDM@ICDM2013
Dec 7, 2013
23Predicting Current User Intent with Contextual Markov Models
Mykola Pechenizkiy, Eindhoven University of Technology

24. Change of Intent as Context Switch
Timeline
t5t0 t3t2 t4
t1
Search
Refine
Search
PaymentClick
Product
View
Search Click
t6
Context ``Find information”
Context ``Buy product”
What is next?
Change of intent?
DDDM@ICDM2013
Dec 7, 2013
24Predicting Current User Intent with Contextual Markov Models
Mykola Pechenizkiy, Eindhoven University of Technology

25. User next action prediction
Search
Refine
Search
PaymentClick
Product
View
Click ?
• What the context is attached to?
o Single action?
o Session/trail? (user)
o A group of sessions (space/time)
• Pattern-mining based approach
Collaboration is welcome!
DDDM@ICDM2013
Dec 7, 2013
25Predicting Current User Intent with Contextual Markov Models
Mykola Pechenizkiy, Eindhoven University of Technology

26. Designing Context-awareness
Predictive
model(s)
PredictionsTraining data
Context-aware Adaptation
Instance set selection
Feature set selection
Feature set expansion
Model selection/weighting
Model adjustment Output correction
if (context == “spring”)
select instances(“spring”)
if (context == “spring”)
select models (“spring”)
if (context == “spring”)
score += 0.1*score
DDDM@ICDM2013
Dec 7, 2013
26Predicting Current User Intent with Contextual Markov Models
Mykola Pechenizkiy, Eindhoven University of Technology

27. Designing Context-awareness
Definitions/
properties/
utilities
[Un]
[Semi]Super
vised
methods
How to
define
context
Context mining:
how to discover context
Instance set selection
Feature set selection
Feature set expansion
Model selection/weighting
Model adjustment Output correction
Contextual features
Contextual categories
Features not predictive alone,
but increasing predictive power
of other features
Descriptors explaining a
significant group of instances
having some distinct behaviour
Subgroup discovery
AntiLDA
Uplift modeling
Actionable attributes

28. Horizontal Partitioning
Users
from
Europe
Users
from
South
America
Session 1 Search Refine Search Click on Banner Product View Payment
Session 3 Product
View
Payment
Session 3 Search Refine Search Refine Search Click on
Banner
Session 4 Search Refine Search Click on Banner Product View Payment
Session 5 Product
View
Click on Banner Search

29. Horizontal Partitioning

30. Two types of behavior:
Ready to buy – (Product View, Payment)
Just browsing – (Search, Refine Search, Click on
Banner)
Session 1 Search Refine
Search
Click on
Banner
Product
View
Payment
Session 2 Product
View
Payment
Session 3 Search Refine
Search
Refine
Search
Click on
Banner
Session 4 Search Refine
Search
Click on
Banner
Product
View
Payment
Session 5 Product
View
Click on
Banner
Search
Vertical Partitioning

31. Session 1 Search Refine
Search
Click on
Banner
Product
View
Payment
Session 2 Product
View
Payment
Session 3 Search Refine
Search
Refine
Search
Click on
Banner
Session 4 Search Refine
Search
Click on
Banner
Product
View
Payment
Session 5 Product
View
Click on
Banner
Search
Two types of behavior:
Ready to buy – (Product View, Payment)
Just browsing – (Search, Refine Search, Click on
Banner)
Vertical Partitioning