A Method for Analyzing User Action Logs

1. A Method for Analyzing User Action Logs Papers - Interception of User's Interests on the Web - User Characteristics Acquisition from Logs with Semantics - Estimation of User Characteristics using Rule-based Analysis of User Logs - Personalized Presentation in Web-Based Information Systems • JaeseokMyung Intelligent Database Systems Lab School of Computer Science & Engineering Seoul National University, Seoul, Korea Center for E-Business Technology Seoul National University Seoul, Korea

2. About Project NAZOU • Tools for acquisition, organization and maintenance of knowledge • NAZOU is proposed by FIIT in Slovakia University to realize the slogan • The result of this research are verified by pilot application about job offers • http://nazou.fiit.stuba.sk/home/index.php • Paper Info. • Barla, M.: Interception of User's Interests on the Web. In V. Wade, H. Ashman, and B. Smyth, editors, 4th Int. Conf. on Adaptive Hypermedia and Adaptive Web-Based Systems, AH'06, pages 435-439, Dublin, Ireland, 2006, Springer, LNCS 4018 • Barla, M., Andrejko, A., Bieliková, M., Tvarožek, M.: User Characteristics Acquisition from Logs with SemanticsIn ISIM ´07 Information Systems and Formal Models: 10th International Conference on Information System Implementation and Modeling. 2nd International Workshop on Formal Models WFM ´07 2007, Hradec nadMoravicí, Czech Republic, pp.103-110, 2007 • Barla, M., Bieliková, M.: Estimation of User Characteristics using Rule-based Analysis of User Logs. In: Data Mining for User Modeling Proceedings of Workshop held at the International Conference on User Modeling UM2007, Corfu, Greece, pp.5-14, 2007 • Tvarožek, M., Barla, M., Bieliková, M. (2007). Personalized Presentation in Web-Based Information Systems. In J. Van Leeuwen, G. F. Italiano, W. van derHoek, H. Sack, C. Meinel, F. Plášil (Ed.), SOFSEM 2007: Proceedings of the 33rd Conference on Current Trends in Theory and Practice of Computer Science. LNCS 4362, pp. 796-807. Harrachov, Czech Republic: Springer-Verlag, Berlin Heidelberg.

3. Architecture of NAZOU Goal : Personalized Knowledge Presentation

4. Components of NAZOU

5. Logging Issue(1) • Client-side Logging • Captures events that occur in the web browser • Captures individual actions which might be missed by server-side • Load, Unload, Click, MouseOver, MouseOut, … • Cached Page Loading Time • Privacy problem might be happened if we use desktop apps • Click • Technologies used : JavaScript, Ajax, SOAP • Input : user input in form of performed action • Output : Log of user actions sent to server • http://nazou.fiit.stuba.sk/home/?page=click

6. Logging Issue(2) • Server-side Logging • Standard web server logs are not suitable for the estimation of individual user characteristics, since they require complicated preprocessing • 1Cust216.tnt1.santa-monica.ca.da.uu.net - -[08/May/1999:12:13:03 -0700] GET /gen/meeting/ssi/next/HTTP/1.0 200 9887 http://www.slac.stanford.edu/Mozilla/3.01-C-MACOS8 (Macintosh; I; PPC)  GET /gen/meeting/ssi/next/ - HTTP/1.0IP • We need to preserve the semantics of user action as possible as we can • SemanticLog • Collaborating with Click (use pre-defined event ontology) • Technologies used : Java, XML, MySQL, Hibernate, Web Services • Input : Event ontology, Semantic events corresponding to user actions • Output : Integrated log of user events in a relational database corresponding to the event ontology schema • http://nazou.fiit.stuba.sk/home/?page=semantic-log

7. User Modeling • Ontology-based User Model used in NAZOU Project • http://nazou.fiit.stuba.sk/home/files/nazou_um.pdf • Stored as triples in RDF repository • FYI, GUMO(The General User Model Ontology)

8. Rule-based Estimation of User Char. Knowledge on user characteristics acquisition is represented by rules

9. Rule Definition • Each rule consists of • Pattern • Consequence • Pattern • Defined as a sequence of event types and other sub-sequences • Consequence • Determines what and how should be changed in the user model when the instance of a pattern is detected • Rules are stored in a file using XML format

10. Pattern Example <sequence id="s010" isContinuous="true" count-of-occurence="1"> <event id="ev020" type="http://fiit.sk#UserLogin"/> <event id="ev021" type="http://fiit.sk#SelectRestriction"/> <sequence id="s011" count-of-occurence="-1“> <event id="ev022" type=http://fiit.sk#SelectRestriction> <context type=http://fiit.sk#SameAsPrevious> <attributes>http://fiit.sk#PropertyUri</attributes> </context> <context type=http://fiit.sk#DifferentThanPrevious> <attribute>http://fiit.sk#restrictionURI</attribute> </context> </event> </sequence> </sequence>

11. Pattern Detection How can we find instances of appropriate patterns

12. User Model Update Update of a user model is driven by changes specified in the consequence part of rule

13. Updated User Model in NAZOU

14. Conclusion • Click • Realize client-side logging • Use Javascript technology (Good for Security Problem) • SemanticLog • Implements server-side logging by means of a web service(SOAP) • Aggregate one common log per user session • Store logs in a relational database • LogAnalyzer • Estimate user characteristics from the acquired user logs • Evaluates incoming events and updates user model • User characteristics can be used for personalized services

15. Evaluation & Discussion Points • Pros • Interesting Ideas & (Well-bounded & Well-positioned) Components • Ontologies & Tools Implementation • Cons • No experiments • Poor examples • Poor explanation • Discussion Points • What kind of characteristics can we get from logs? • How can we model those user characteristics? • How can we estimate those characteristics? • What are possible services from the user model?