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Jtel 2010 na li
Jtel 2010 na li
Jtel 2010 na li
Jtel 2010 na li
Jtel 2010 na li
Jtel 2010 na li
Jtel 2010 na li
Jtel 2010 na li
Jtel 2010 na li
Jtel 2010 na li
Jtel 2010 na li
Jtel 2010 na li
Jtel 2010 na li
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Jtel 2010 na li

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  • Trust propagates layer by layer, until reaching the propagate distance we predefine.A “Web of Trust” is constructed in this way.
  • Different weights and propagate distances are tried.On this test set, the change of trust weights for relationships doesn’t make a significant difference in the results of rating prediction.We get an optimal propagate distance value, which indicates that, instead of improving the prediction results, increasing the size of trust network might add noise, leading to bigger prediction error.
  • Different weights and propagate distances are tried.On this test set, the change of trust weights for relationships doesn’t make a significant difference in the results of rating prediction.We get an optimal propagate distance value, which indicates that, instead of improving the prediction results, increasing the size of trust network might add noise, leading to bigger prediction error.
  • Transcript

    • 1. Trust Modeling and Evaluation in Web 2.0 Collaborative Learning Social Software
      JTEL 2010 June 7-June 11
      Na Li
      Swiss Federal Institute of Technology in Lausanne (EPFL)
    • 2. Outline
      Research Questions
      Current Progress
      Future Work
    • 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
    • 4. Research Questions
      Trust Measurement
      Evaluate quality of user-generated content
      Recommend useful resources
      Privacy management
    • 5. Current Progress
      Trust-based rating prediction
      Quality evaluation in open learning environment
      Filter helpful learning resources, people and group activities
    • 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.
    • 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?
    • 8. Trust-Based Rating Prediction Approach
      Trust propagation
      Propagation distance (PD)
      Bob
      Commented by
      Article
      Rated by
      Sara
      Create
      Is Member
      Has Member
      French Learning Activity
      Luis
      Alice
      Rate
      Video
      Rated by
      Ben
      Created by
      Jack
      Propagate
      Propagate
      Propagate
      PD
    • 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
    • 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
    • 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
    • 12. Future Work
      Future deploy and evaluation will be conducted in a collaborative learning platform, namely Graaasp(graaasp.epfl.ch)
      Trust-based privacy management
    • 13. Questions?

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