trust-based rating prediction for recommendation in web 2.0 collaborative learning social...
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Presented at ITHET 2010TRANSCRIPT
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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
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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
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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
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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
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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
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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
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Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland
3A Interaction Model
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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.
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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?
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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
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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
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Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland
Trust-Based Rating Prediction Approach
• Indirect Trust (IT) Inference
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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
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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
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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
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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)
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Swiss Federal Institute of Technology in Lausanne EPFL, CH-1015 Lausanne, Switzerland
Questions?