The document summarizes research on modeling and evaluating trust in Web 2.0 collaborative learning environments. It discusses researching how to determine what and who to trust given large amounts of user-generated content. A trust-based rating prediction approach is presented that uses a "Web of Trust" to predict ratings by propagating trust through user relationships and weighting ratings by trust values. The approach is evaluated on a dataset with promising results, and future work aims to deploy and test the approach on an online learning platform.
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Trust Modeling Web 2.0 Collaborative Learning
1. JTEL 2010 June 7-June 11
Trust Modeling and Evaluation in Web 2.0
Collaborative Learning Social Software
Na Li
Swiss Federal Institute of Technology in Lausanne (EPFL)
Swiss Federal Institute of Technology in Lausanne
EPFL, CH-1015 Lausanne, Switzerland
3. Research Questions
• Lots of Web 2.0 learning environments bring about large
amount of user-generated content
▫ What should we trust?
▫ Who should we trust?
RSS Feeds
Pictures Pictures
Wiki Pages Documents
Videos
Swiss Federal Institute of Technology in Lausanne
EPFL, CH-1015 Lausanne, Switzerland
4. Research Questions
• Trust Measurement
▫ Evaluate quality of user-generated content
▫ Recommend useful resources
▫ Privacy management
Swiss Federal Institute of Technology in Lausanne
EPFL, CH-1015 Lausanne, Switzerland
5. Current Progress
• Trust-based rating prediction
▫ Quality evaluation in open learning
environment
▫ Filter helpful learning resources, people and
group activities
Swiss Federal Institute of Technology in Lausanne
EPFL, CH-1015 Lausanne, Switzerland
6. Trust-Based Rating Prediction Approach
• Basic idea
▫ What influences rating opinion: similarity and
familiarity
▫ People tend to trust the opinions of
acquaintance and those having similar
interests and tastes.
Swiss Federal Institute of Technology in Lausanne
EPFL, CH-1015 Lausanne, Switzerland
7. Trust-Based Rating Prediction Approach
• Trust measurement
▫ Multi-relational trust metric
▫ Build a “Web of Trust” for a particular user using
heterogeneous types of relationships
Trust
How Much?
Swiss Federal Institute of Technology in Lausanne
EPFL, CH-1015 Lausanne, Switzerland
8. Trust-Based Rating Prediction Approach
• Trust propagation
Bob
• Propagation distance (PD) ente
d by
m
Com
Rated by
e Article Sara
Creat
Is Member French Has Member
Alice Learning Luis
Activity
Rated by
Video Ben
Jack
Propagate Propagate Propagate
PD
Swiss Federal Institute of Technology in Lausanne
EPFL, CH-1015 Lausanne, Switzerland
9. Trust-Based Rating Prediction Approach
• Rating prediction from a user to an item
▫ Using user’s “Web of Trust”
▫ People in “Web of Trust” are seen as trustable
▫ Average of all the rating scores given by trustable
people, weighted by their trust value
Swiss Federal Institute of Technology in Lausanne
EPFL, CH-1015 Lausanne, Switzerland
10. Evaluation and Results
• Using Remashed data set
▫ 50 users, 6000 items, 3000 tags and 450 ratings
▫ “Leave-one-out” method
▫ Compare “predicted score – actual score” deviation of
trust-based prediction and simple average
Swiss Federal Institute of Technology in Lausanne
EPFL, CH-1015 Lausanne, Switzerland
11. Evaluation and Results
• Change parameters
▫ Weights for relationships doesn’t make a significant
difference in rating prediction
▫ Increasing size of trust network might add noise, lead
to bigger prediction error
Swiss Federal Institute of Technology in Lausanne
EPFL, CH-1015 Lausanne, Switzerland
12. Future Work
• Future deploy and evaluation will be conducted in a
collaborative learning platform, namely Graaasp
(graaasp.epfl.ch)
• Trust-based privacy management
Swiss Federal Institute of Technology in Lausanne
EPFL, CH-1015 Lausanne, Switzerland
13. Questions?
Swiss Federal Institute of Technology in Lausanne
EPFL, CH-1015 Lausanne, Switzerland