Abstract—In many web information systems like e-shops and information portals predictive modeling is used to understand user intentions based on their browsing behavior. User behavior is inherently sensitive to various contexts. Identifying such relevant contexts can help to improve the prediction performance. In this work, we propose a formal approach in which the context
discovery process is defined as an optimization problem. For simplicity we assume a concrete yet generic scenario in which context is considered to be a secondary label of an instance that is either known from the available contextual attribute (e.g. user location) or can be induced from the training data (e.g. novice vs. expert user). In an ideal case, the objective function of the optimization problem has an analytical form enabling us
to design a context discovery algorithm solving the optimization problem directly. An example with Markov models, a typical approach for modeling user browsing behavior, shows that the derived analytical form of the optimization problem provides us with useful mathematical insights of the problem. Experiments with a real-world use-case show that we can discover useful contexts allowing us to significantly improve the prediction of
user intentions with contextual Markov models.
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Predicting Current User Intent with Contextual Markov Models
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
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
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
DDDM@ICDM2013
Dec 7, 2013
11Predicting Current User Intent with Contextual Markov Models
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
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
DDDM@ICDM2013
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