trust modeling and evaluation in web 2.0 collaborative learning social software_jtel 2010_na li
DESCRIPTION
Presented at JTEL Summer School 2010TRANSCRIPT
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Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland
Trust Modeling and Evaluation in Web 2.0 Collaborative Learning Social Software
Na Li
Swiss Federal Institute of Technology in Lausanne (EPFL)
JTEL 2010 June 7-June 11
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Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland
Outline
• Research Questions • Current Progress • Future Work
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Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland
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
Documents
Videos
Wiki Pages
Pictures
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Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland
Research Questions • Trust Measurement ▫ Evaluate quality of user-generated content ▫ Recommend useful resources ▫ Privacy management
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Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland
Current Progress
• Trust-based rating prediction ▫ Quality evaluation in open learning
environment ▫ Filter helpful learning resources, people and
group activities
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Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland
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.
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Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland
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?
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Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland
Trust-Based Rating Prediction Approach • Trust propagation • Propagation distance (PD)
Alice
French Learning Activity
Is Member
Article Create
Video
Propagate
Luis Has Member
Rated by Sara
Rated by Ben
Bob
Commented by
Jack Propagate Propagate
PD
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Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland
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
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Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland
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
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Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland
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
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Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland
Future Work
• Future deploy and evaluation will be conducted in a collaborative learning platform, namely Graaasp(graaasp.epfl.ch)
• Trust-based privacy management
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Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland
Questions?