trust-based rating prediction for recommendation in web 2.0 collaborative learning social...
DESCRIPTION
Presented at ITHET 2010TRANSCRIPT
Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland
Trust-Based Rating Prediction for Recommendation in Web 2.0 Collaborative Learning Social Software
Na Li, Sandy El Helou, Denis Gillet
Real-Time Coordination and Distributed Interaction Systems (ReAct) Automatic Control Lab, Swiss Federal Institute of Technology in Lausanne
ITHET 29th April – 1st May 2010, Cappadocia, Turkey
Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland
Outline
• Introduction • Collaborative Learning Domain • 3A Interaction Model • Trust-Based Rating Prediction Approach • Evaluation and Results • Conclusion and Future Work
Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland
Introduction • Web 2.0 social software ▫ A large amount of user generated content ▫ New challenge: selection of useful resources
RSS Feeds
Pictures
Documents
Videos
Wiki Pages
Pictures
Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland
Introduction
• Rating systems ▫ Evaluate quality of content in open environment ▫ Provide recommendation for different users
Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland
Introduction • Rating systems – application level
• Rating systems – academic research level ▫ TidalTrust (J. Golbeck), MoleTrust(P. Massa) ▫ User explicitly specifies a trust value towards another user ▫ Build trust network, Random walk in trust network ▫ Personalized rating prediction
Epinions 1 to 5 stars A set of aspects for shops and products (ordering, delivery, service) Status for members (Advisor, Top reviewer, Category Lead)
ePractice.eu Use “Kudos” to measure the activity of members Award a number of “Kudos” according to each user action
Everything2 “Positive” and “Negative” votes for articles Users’ ranking according to their contribution
Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland
Collaborative Learning Domain
• Collaborative learning environment ▫ Unlike e-commerce and review sites ▫ Gift economy
• Rating systems ▫ Evaluate user generated content ▫ Filter helpful learning resources, peers and group
activities ▫ Personalized rating prediction for recommendation
Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland
3A Interaction Model
Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland
Trust-Based Rating Prediction Approach
• Objective ▫ Build users’ trust network using 3A graph structure ▫ Personalize the rating prediction ▫ Infer trust value in an implicit way
• 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 Inference ▫ Direct trust ▫ Indirect trust
Trust
How Much?
Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland
Trust-Based Rating Prediction Approach
• Direct trust (DT): derived from a particular type of relationship
W (Membership): weight of “membership” relationship N (Alice, Membership): number of group activities Alice joins
Alice Advanced
Algorithms Group Activity
Is Member of
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
• Indirect Trust (IT) Inference
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
Conclusion and Future Work
• Propose a trust-based rating prediction approach, inferring trust in an implicit way
• Provide personalized rating prediction so as to evaluate user-generated content in collaborative learning environment
• Future deploy and evaluation will be conducted in a collaborative learning platform, namely Graaasp(graaasp.epfl.ch)
Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland
Questions?