Content Recommendation Based on Data Mining in Adaptive Social Networks
Trust-Based Rating Prediction for Recommendation in Web 2.0 Collaborative Learning Social Software_Na Li
1. ITHET 29th April – 1st May 2010, Cappadocia, Turkey
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
Swiss Federal Institute of Technology in Lausanne
EPFL, CH-1015 Lausanne, Switzerland
2. 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
3. Introduction
• Web 2.0 social software
▫ A large amount of user generated content
▫ New challenge: selection of useful resources
RSS Feeds
Pictures Pictures
Wiki Pages Documents
Videos
Swiss Federal Institute of Technology in Lausanne
EPFL, CH-1015 Lausanne, Switzerland
4. 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
5. Introduction
• Rating systems – application level
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
• 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
Swiss Federal Institute of Technology in Lausanne
EPFL, CH-1015 Lausanne, Switzerland
6. 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
7. 3A Interaction Model
Swiss Federal Institute of Technology in Lausanne
EPFL, CH-1015 Lausanne, Switzerland
8. 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
9. 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
10. Trust-Based Rating Prediction Approach
• Direct trust (DT): derived from a particular type
of relationship
Is Member of Advanced
Alice Algorithms Group
Activity
W (Membership): weight of “membership” relationship
N (Alice, Membership): number of group activities Alice joins
Swiss Federal Institute of Technology in Lausanne
EPFL, CH-1015 Lausanne, Switzerland
11. 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
12. Trust-Based Rating Prediction Approach
• Indirect Trust (IT) Inference
Swiss Federal Institute of Technology in Lausanne
EPFL, CH-1015 Lausanne, Switzerland
13. 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
14. 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
15. 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
16. 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
17. Questions?
Swiss Federal Institute of Technology in Lausanne
EPFL, CH-1015 Lausanne, Switzerland