Download Interception of User`s Interests on the Web

Interception of
User’s Interests
on the Web
Michal Barla
Supervisor: prof. Mária Bieliková
[email protected]
Motivation
• Adaptation is based on user model
• Manual filling of user model brings several issues
– Not what a user really wants to do
– User may over/under estimate herself
– User may not know exactly some needed
characteristics
• Goal: Estimate user characteristics automatically
by analyzing user behavior within a system
DC AH 2006
Interception of User's Interests on
the Web
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Process
Data about user
Collects
User Modeling
Processes
System
User Model
Processes
Adaptation
Adaptation effect
Peter Brusilovsky. Methods and techniques of adaptive hypermedia.
User Modeling and User-Adapted Interaction, 6(2-3):87–129, 1996.
DC AH 2006
Interception of User's Interests on
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Data Collection
• We create logs of user activities
• Usually done on server side
– Advantage: always available
– Issue: server is not aware of all performed actions
• Back button and browser cache
• Active elements on a page – e.g. hover
• Monitoring on client side
– Use of client web technologies (JavaScript, Java applets)
– Advantage: we can capture all actions with exact timestamps
– Issue: we have no control on execution of logging tool
DC AH 2006
Interception of User's Interests on
the Web
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Data Collection – our approach
• Combination of serverside and client-side
logging
Client
Click
XmlHTTPRequest/SOAP
• Client Side Action
Recorder – Click
Presentation layer
– Monitoring on client side
• SemanticLog
SemanticLog
SOAP
Presentation
tools
Log of user’s activity
– Specialized server side
logging tool
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HTTP request/response
Other layers of the system
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Click
• Based on JavaScript
– Native access to DOM
• Captures events fired by browser
– Load, Unload, Click, Mouseover,…
• For each event, it records
– Type of event
– Timestamp
– Event context (e.g. what link was pressed)
• Event handling based on W3C DOM Level 2 Event Specification
– Easy integration into existing static pages and dynamic pages
• Communication with server is done asynchronously using AJAX
DC AH 2006
Interception of User's Interests on
the Web
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Data analysis - challenge
• No direct connection between user behavior and user
characteristics
• User may behave in contrast with her characteristics
(which also characterize such user)
• People are changing  characteristics are changing
• Goal: Estimate characteristics
– Each characteristic has some confidence
– Not all possible characteristics (suitable for a set of domain
characteristics)
DC AH 2006
Interception of User's Interests on
the Web
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Data analysis - approaches
• Analysis of navigation
– What path did user choose to reach desired information?
• Analysis of user feedback
– Explicit or implicit
– What are the reasons of different ratings?
• Analysis of consistent behavior
– Does user behave according to previous sessions?
– Is user model still valid?
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Interception of User's Interests on
the Web
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Analysis of navigation
Real Usage
Data
Contain
Implies
Navigation
Model
Defines goal for
Application
Domain
Usage Patterns
Are evaluated according to
Heuristics
Estimate attributes of
Instance of the
User Model
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Interception of User's Interests on
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Usage patterns identification
• Usage patterns
– pre-defined according to navigation model of a web
site
• Pattern lookup ~ sequence matching
• Usage data
– Stored in a suffix tree structure
• Suffix trie = compressed trie (from “retrieval”)
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Interception of User's Interests on
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Analysis of user feedback
• Searching for implicit feedback patterns on
information objects
– Selection, duration, retention
• Evaluation of feedback  rating
• Rating does not give user characteristics
– Why user rated A differently than B?
– Why user rated P same as Q?
– We get characteristics by comparing concepts
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Interception of User's Interests on
the Web
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Concept Comparing - example
• Two similar job offers differs only in duty location
• They get different rating
 We can raise the relevance of duty location
characteristic. We can also estimate desired/unwanted
values.
• Two different job offers have the same rating
 If we find some common aspect, we can raise relevance
of appropriate characteristic
DC AH 2006
Interception of User's Interests on
the Web
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Analysis of consistent behavior
• Sequential patterns mining on previous user sessions
– Typical user behavior
– Actual session should be mapped to some pattern
• Reasons of inconsistent behavior
– User is in “special” mood, does not follow presumed goal
– User is looking for information on behalf of somebody else
– User has changed
• It has been a long time from previous session
• We invalidate the model and start over
DC AH 2006
Interception of User's Interests on
the Web
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LogAnalyzer
ConceptComparer
Database
Inconsistent
behavior
detection
Feedback
evaluator
Data
preprocessing
Domain
ontology
User
Model
Updater
Pattern
detection
Log Analyzer
Logs
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Behavior
patterns
User
model
Heuristics
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Conclusions
• User modeling based on user behavior analysis
• Acquiring of activity logs on client and server side
– JavaScript based logging tool – Click
• Various approaches to analysis of acquired logs
– Navigation
– Feedback ~ Concept Comparing
– LogAnalyzer
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