Mining User's Interest from Reading Behavior in E-learning System

A method based on reading behavior is proposed for capturing user's interest in E-learning System. A topic ontology is necessary to be predefined which can be used as the reference to construct user's interest model. A behavior table is generated by profile agent to record user's behaviors including underline, highlight, circle, annotation and bookmark. We adopt a behavior matrix and weight matrix to compute the user's interests in each leaf topic in the topic ontology. The user's interest about the topic ontology can be extended via support factor. An experiment is conducted to estimate the performance of our approach. It shows that our approach can capture user's interest precisely.

[1]  Akrivi Katifori,et al.  Creating an Ontology for the User Profile: Method and Applications , 2007, RCIS.

[2]  Zhonghua Yang,et al.  A learning multi-agent system for personalized information filtering , 2003, Fourth International Conference on Information, Communications and Signal Processing, 2003 and the Fourth Pacific Rim Conference on Multimedia. Proceedings of the 2003 Joint.

[3]  Peter Brusilovsky,et al.  User modeling and user adapted interaction , 2001 .

[4]  Ryen W. White.  Implicit feedback for interactive information retrieval , 2005, SIGF.

[5]  Oliver Scheuer,et al.  Using Action Analysis in ActiveMath to Estimate Student Motivation , 2005, LWA.

[6]  Marko Grobelnik,et al.  User Profiling for Interest-focused Browsing History , 2005 .

[7]  Qingtian Zeng,et al.  Using a User-Interactive QA System to Capture Student's Interest and Authority About Course Content , 2006, ICWL.

[8]  Bernard J. Jansen,et al.  Automated evaluation of search engine performance via implicit user feedback , 2005, SIGIR '05.

[9]  Philip K. Chan,et al.  Learning implicit user interest hierarchy for context in personalization , 2003, IUI.

[10]  Paul Libbrecht,et al.  ActiveMath: A Generic and Adaptive Web-Based Learning Environment , 2001 .

[11]  Masatoshi Yoshikawa,et al.  Adaptive web search based on user profile constructed without any effort from users , 2004, WWW '04.

[12]  I. B. Crabtree,et al.  Automatic Learning of User Profiles — Towards the Personalisation of Agent Services , 1998 .

[13]  Stuart E. Middleton,et al.  Ontological user profiling in recommender systems , 2004, TOIS.