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Discovering temporal hidden contexts in web sessionsfor user trail prediction
Julia Kiseleva (Eindhoven University of Technology),Hoang Thanh Lam (Eindhoven University of Technology),Mykola Pechenizkiy (Eindhoven University of Technology),Toon Calders (Université Libre de Bruxelles)
User intention prediction
Search Refine Search
PaymentClick Product
ViewClick ?
What is next?
• Does exist any contextual information?• How we can discover it?• How we can utilize it?
Contextual Partitioning
• Approaches to create local models:o Horizontal partitions
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
User navigation graph
Search
Refine Search
Payment
Click on Banner
Product View
1.0 2/3
1/3
1/21/4
Drop out
3/4
1/4
1
1/4
Horizontal Partitioning
• Approaches to create local models:o Horizontal partitiono Vertical partition :
• Two types of behavior:o Ready to buy – (Product View, Payment)
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
Contextual Partitioning
• Approaches to create local models:o Horizontal partitiono This work is about Vertical partition:
• Two types of behavior:o Ready to by – (Product View, Payment)o 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
Contextual Partitioning
• Approaches to create local models:o Horizontal partitiono Vertical partition:
• Two types of behavior:o Ready to buy – (Product View, Payment)o 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
Contextual Partitioning
Problem Description
Timeline
Search Refine Search
PaymentClick Product
ViewSearch Click
Context ``Find information”
Context ``Buy product”
What is next?Probably user will change
intent?
Problem Description
Timeline
Search=A
Refine Search
=B
PaymentClick
=C
Product View
=D
Search Click
Context ``Find information”
Context ``Buy product”
Context-Awareness Example
Contextual Feature:User intent
DATA
Contextual Categories
Individual Models
Mapping G
Mapping H Context
Discovery
Ready to buy
Just browsing
Context-Awareness
………………
Contextual features
DATA Environment
Contextual Categories
Individual Learners
Mapping G
Mapping H
Context Discovery
Context-Awareness: (G,H)
Temporal Context-Awareness
G H
Temporal Context-Awareness: (G,H,ti)
……
..
G
G
H
H
Web
Sessio
n
S
Contextual features
Contextual Categories
Individual Models
Discovery hidden contexts
Web log
Train:To train
predictive models
Validation:To find Best
clusters
Test:To derive final
accuracy
To find a “Best”
clusters
Calculate final accuracy
To train local predictive
models
Optimization problem
a b c d abababababcdcdababcdcdcd
The number of true predictions = 0
a b c d
1.0 1.0 1.0
1.0
Hierarchical clustering
a b c d
ab
abababababcdcdababcdcdcd
The number of true predictions = 12
a b c d
1.0
1.0
1.0 1.0
Hierarchical clustering
a b c d
ab
abababababcdcdababcdcdcd
The number of true predictions = 20
a b c d
1.0
1.0
1.0
1.0
cd
Hierarchical clustering
a b c d
ab
abababababcdcdababcdcdcd
The number of true predictions = 20
a b c d
1.0
1.0
1.0
1.0
cd
abcd
Stop as long as no additional prediction benefit of merging
Hierarchical clustering
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
DatasetDate May 2012
#sessions
350.618
#requests
1.775.711
#sessions from Eu
159.991
Results
Explaining the results
eb C d
a1.0
1.0
0.9
0.1
1.0
1.0
Explaining the results
eb C d
a1.0
1.0
0.9
0.1
1.0
1.0
Explanation:1. Users’ behavior doesn’t follow Markov
property2. Ambiguous Transition Matrix
Explaining the results
University on nearby universities
Related Programs
0.315
0.299
Quick Search
0.306
Related Programs
Related Programs
…
Click on Program
0.283
…
Resulted ClustersId Summary Cluster
1 Intensive Search Basic Search, Refine Search, EmptySearch Result
2 Explore informationrelated toprogram
Program impression in search result,Banner click, Program click ,Click on university link
3 Start ofbrowsing
University Spotlight impression,Quick search
4 Explorecountry information
File view, Click on country link
5 Exploresearch result
Program impressions in search results,University impression onnearby universities
6 Explore program Program in landing page, Submitinquiry
7 Outlier Submit question, X-node
Site Map• Page is represented as set of possible actions
o Example: Homepage is (Quick Search, University Spot light impression, Question Submit)
o Calculate Jaccard similarity between Page and Cluster
Site map
Conclusion• We formulate the problem of temporal context
discovery as an optimization problem.
• A hierarchical clustering method is proposed to determine the optimal number of hidden contexts and mine temporal contexts.
• We show a real-world use case in which the contexts as we defined them do exist and are useful for prediction.
Future works• Testing our method on another datasets
• Introducing a mechanism for detecting context switching within a web-session
• Considering multidimensional contextualfeatures.
Thank you!• Context identification and integration
it into prediction models• Accurately predicting users’ desired
actions and understanding behavioral patterns of users in various web-applications
• Personalization and adaptation to diverse customer need and preferences
• Accounting for the practical needs within the considered application areas.
Conclusion• We formulate the problem of temporal context
discovery as an optimization problem.
• A hierarchical clustering method is proposed to determine the optimal number of hidden contexts and mine temporal contexts.
• We show a real-world use case in which the contexts as we defined them do exist and are useful for prediction.
Questions?
Outline• Problem description• Def. context-aw => temporal CA• Motivational example• Objectives• Method: HC• Data• Results• Summary• Conclusion• Future works
• Web sessions:• Type of events:• Web session is an
ordered sequence of events:
• Space of contextual features:
• Predictive model:• Evaluation function:• Contextual categories: