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 HSIAOFeb.23.2007

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 brow

sing behavior Scheduler: regularly command Wrapper agent to col

lect 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 schedu

le of different topic (predefined) No ranking for 1st 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