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Personalized Course Navigation Based on Grey Relational Analysis

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.23.2007. agenda. Introduction/motivation

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Personalized Course Navigation Based on Grey Relational Analysis

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  1. 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.23.2007

  2. agenda • Introduction/motivation • Course Recommending Procedure • Results & Evaluation • Suggestions

  3. 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

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

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

  6. 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

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

  8. 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 1st time user

  9. 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

  10. 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

  11. Example:

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

  13. 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

  14. 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

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