Personalized Course Navigation Based on Grey Relational Analysis Han-Ming Lee, Chi-Chun Huang, Tzu- Ting Kao (Dept. of Computer Science and Information Engineering, National Taiwan University of Science and technology) Presented by Sharon HSIAO Feb

agenda Introduction/motivation Course Recommending Procedure Results & Evaluation Suggestions

Introduction Aim: to provide a personalized information recommendation system that dynamically reflects users’ interests Focus: model users’ interests without explicit rating Content-based personalized technique WGRA (Weighted Grey Relational Analysis) Coursebot System: distance learning system

Coursebot Agent-based system Gather course materials from internet Make intelligent learning recommendations Classification methods: style retrieval techniques to extract features

5 components: wrapper agent, course constructor, query agent, interface agent, scheduler

Coursebot 5 components Wrapper Agent: collect course material webpages, then classify them by topics in given subjects Course Constructor: organize webpages from course database as the materials in response to users’ queries Query Agent: retrieve and expand the query from db Interface Agent: learns profiles based on users browsing behavior Scheduler: regularly command Wrapper agent to collect materials

Personalized Course Navigation Learning and ranking based on user profiles Use WGRA measure to analyze user preferences

How does it actually work? Interact (Query Agent, Interface agent) Time spent on a page (>15 mins is discarded) Length of each page in bytes is recorded Feature vector is used (A = D[f1,f2,…,fm]) Course Display (Query Agent, Course constructor) Rank by revised user profiles and learning schedule of different topic (predefined) No ranking for 1 st time user

WGRA (weighted Grey Relational Analysis) To analyze degrees of relevance among a visited page Row: individual feature of the document Column: the degree of Grey relation assigned to the feature fi between each doc. in Ti and D1

The higher degree γi1 between Di & D1 means That these two docs are related to each other A longer visit to a given page, the user Probably has higher interest According to the interests of the doc(browsing time &length of page), apply adjustment to WGR grade vector

Example:

Experiment results 7 topics “Neural Networks” 1032 related webpages (spider) 128 features (style retrieval) 69 Ratings (graduate students who had taken NN)

conclusion The proposed method was not significantly different from other algorism User profiles are easily maintained Low complexity Ease to add knowledge suitable for online personalized analysis

Suggestions/notes Users are restricted to receiving documents similar to related items seen previously by other user Users’ interests concerning various course materials can be easily modeled