Trust Modeling and Evaluation in Web 2.0 Collaborative Learning Social Software_JTEL 2010_Na Li

JTEL 2010 June 7-June 11Trust Modeling and Evaluation in Web 2.0Collaborative 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

Outline• Research Questions• Current Progress• Future Work 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 Pictures Wiki Pages Documents Videos 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 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 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. 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? Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland

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

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

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

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

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

Questions? Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland

Trust Modeling and Evaluation in Web 2.0 Collaborative Learning Social Software_JTEL 2010_Na Li

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Trust Modeling and Evaluation in Web 2.0 Collaborative Learning Social Software_JTEL 2010_Na Li Presentation Transcript