trust modeling and evaluation in web 2.0 collaborative learning social software_jtel 2010_na li

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

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

Documents

Videos

Wiki Pages

Pictures

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 •  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

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?