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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 Intentwith 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
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@ICDM2013Dec 7, 2013
2Predicting Current User Intent with Contextual Markov ModelsMykola Pechenizkiy, Eindhoven University of Technology
Understanding user needs
DDDM@ICDM2013Dec 7, 2013
3Predicting Current User Intent with Contextual Markov ModelsMykola Pechenizkiy, Eindhoven University of Technology
Let’s give it a try…
DDDM@ICDM2013Dec 7, 2013
4Predicting Current User Intent with Contextual Markov ModelsMykola Pechenizkiy, Eindhoven University of Technology
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@ICDM2013Dec 7, 2013
5Predicting Current User Intent with Contextual Markov ModelsMykola Pechenizkiy, Eindhoven University of Technology
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@ICDM2013Dec 7, 2013
6Predicting Current User Intent with Contextual Markov ModelsMykola Pechenizkiy, Eindhoven University of Technology
User Intent Modeling: How?
Model L
population(source)
Historicaldata
labels
label?
1. training
2.
2. application
X
y
X'
y'
Training:
y = L (X)
Application:use Lfor an unseen data
y' = L (X')
labels
Testingdata
DDDM@ICDM2013Dec 7, 2013
7Predicting Current User Intent with Contextual Markov ModelsMykola Pechenizkiy, Eindhoven University of Technology
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@ICDM2013Dec 7, 2013
8Predicting Current User Intent with Contextual Markov ModelsMykola Pechenizkiy, Eindhoven University of Technology
Context in Diagnostics Not predictive alone but a subset of features with the contextual attribute(s) becomes (much) more predictive
Time of the daycontext
no context
DDDM@ICDM2013Dec 7, 2013
9Predicting Current User Intent with Contextual Markov ModelsMykola Pechenizkiy, Eindhoven University of Technology
Context in Marketing P(Purchase|gender=“male”)=P(Purchase|gender=“female”)ModelMale~f(relevance); ModelFemale~f(perceived value)
gendercontext
no context
Male
Female
buy
buy
relevance
relevance
buy
don’t
don’t
don’t
gender
DDDM@ICDM2013Dec 7, 2013
10Predicting Current User Intent with Contextual Markov ModelsMykola Pechenizkiy, Eindhoven University of Technology
Environment/Context
Model L
population
Training:
??
Application:
y' = Lj (X')Lj <= G(X',E)
X'
y'
Historicaldata
labels
X
y
label?
Context-Awareness as Meta-learning
labels
Testdata
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11Predicting Current User Intent with Contextual Markov ModelsMykola Pechenizkiy, Eindhoven University of Technology
Learning Classifiers & Context
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12Predicting Current User Intent with Contextual Markov ModelsMykola Pechenizkiy, Eindhoven University of Technology
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?
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14Predicting Current User Intent with Contextual Markov ModelsMykola Pechenizkiy, Eindhoven University of Technology
IEEE CBMS 2010Perth, Australia
Handling Concept Drift in Medical Applications: Importance, Challenges and Solutions© M. Pechenizkiy and I. Zliobaite
15
• Context-aware ranking of search results
• Drop-out prediction/prevention
• Next action prediction
Mastersportal.eu - Homepage
Quick Search
Banner Click
Universities in the spotlight
Mastersportal.eu - Search
Refine Search
Click on Program is Search Result
Click on University
Click on Country
User Navigation Graph
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)
Implicit Context
Discover clusters in the graph using community detection algorithm
c1 = Novice users
c1 = Experienced
usersC = user type
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20Predicting Current User Intent with Contextual Markov ModelsMykola Pechenizkiy, Eindhoven University of Technology
Dataset
DateSource of information
May 2012Mastersportal.eu
#sessions 350.618#requests 1.775.711
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21Predicting Current User Intent with Contextual Markov ModelsMykola Pechenizkiy, Eindhoven University of Technology
Publicly available at:http://www.win.tue.nl/~mpechen/projects/capa
Accuracy Results
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22Predicting Current User Intent with Contextual Markov ModelsMykola Pechenizkiy, Eindhoven University of Technology
user location
user type
Global vs. explicit vs. implicit vs. random contexts
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23Predicting Current User Intent with Contextual Markov ModelsMykola Pechenizkiy, Eindhoven University of Technology
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 graphDDDM@ICDM2013Dec 7, 2013
24Predicting Current User Intent with Contextual Markov ModelsMykola Pechenizkiy, Eindhoven University of Technology
Change of Intent as Context Switch
Timeline
Search Refine Search PaymentClick Product
View Search Click
Context ``Find information”
Context ``Buy product”
What is next?Change of intent?
DDDM@ICDM2013Dec 7, 2013
25Predicting Current User Intent with Contextual Markov ModelsMykola Pechenizkiy, Eindhoven University of Technology
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@ICDM2013Dec 7, 2013
26Predicting Current User Intent with Contextual Markov ModelsMykola Pechenizkiy, Eindhoven University of Technology
Designing Context-awareness
Predictive model(s) PredictionsTraining data
Context-aware Adaptation
Instance set selectionFeature set selectionFeature 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
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27Predicting Current User Intent with Contextual Markov ModelsMykola Pechenizkiy, Eindhoven University of Technology
Designing Context-awareness
Definitions/properties/
utilities
[Un] [Semi]Super
visedmethods
How to define
context
Context mining: how to discover context
Instance set selectionFeature set selectionFeature set expansion Model selection/weighting
Model adjustment Output correction
Contextual featuresContextual categories
Features not predictive alone, but increasing predictive power of other featuresDescriptors explaining a significant group of instances having some distinct behaviour
Subgroup discoveryAntiLDAUplift modelingActionable attributes
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
Horizontal Partitioning
Two types of behavior:Ready to buy – (Product View, Payment)Just browsing – (Search, Refine Search, Click on
Banner) Session 1 Search Refine
SearchClick 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
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
![Learning Message-Passing Inference Machines for Structured ... · Contextual Classification with Functional Max-Margin Markov Networks. CVPR 2009. [5] A. Kulesza & F. Pereira. Structured](https://img.pdfslide.us/doc/110x75/5f6f1273170dbf313e581dc1/learning-message-passing-inference-machines-for-structured-contextual-classification.jpg)
