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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
- 2. Outline • Research Questions • Current Progress • Future Work 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