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Interception of User’s Interests on the Web

Interception of User’s Interests on the Web. Michal Barla. barla @fiit.stuba.sk. Supervisor : prof. Mária Bieliková. 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

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Interception of User’s Interests on the Web

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  1. Interception of User’s Interestson the Web Michal Barla barla@fiit.stuba.sk Supervisor: prof. Mária Bieliková

  2. 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 Interception of User's Interests on the Web

  3. Process Peter Brusilovsky. Methods and techniques of adaptive hypermedia. User Modelingand User-Adapted Interaction, 6(2-3):87–129, 1996. Interception of User's Interests on the Web

  4. 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, hiding • 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 Interception of User's Interests on the Web

  5. Data Collection – our approach • Combination of server-side and client-side logging • Client Side Action Recorder – Click • Monitoring on client side • SemanticLog • Specialized server side logging tool Interception of User's Interests on the Web

  6. 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 Interception of User's Interests on the Web

  7. Data analysis - challenge • No direct connection between user behavior and user characteristics • User may behave in contrast with what seems to be logic (which can also be stored as a characteristic ) • People are changing  characteristics are changing • Goal: Estimate characteristics • Each characteristic has some confidence • Not all possible characteristics (suitable for a set of domainspecific characteristics) Interception of User's Interests on the Web

  8. 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? Interception of User's Interests on the Web

  9. Analysis of navigation Interception of User's Interests on the Web

  10. Usage patterns identification • Usage patterns • pre-defined according to navigation model of a web site • Pattern lookup ~ sequence matching • Using a suffix tree data structure • Suffix trie = compressed trie (from “retrieval”) Interception of User's Interests on the Web

  11. Analysis of user feedback • Searching for implicit feedback patterns on information objects • Selection, duration, retention of information • 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 Interception of User's Interests on the Web

  12. 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 Interception of User's Interests on the Web

  13. 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 Interception of User's Interests on the Web

  14. LogAnalyzer Interception of User's Interests on the Web

  15. 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 Interception of User's Interests on the Web

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