1. SREC: A Social Behaviour Based Recommender for Online Communities
Surya Nepal, Cécile Paris and Sanat Kumar Bista
TRUM 2012 July 2012

2. Aim and Context
Our context: an online community to support welfare recipients
General aim: to build trusted online communities to deliver government services to citizens:
Bring people together so that they can support each other
Provide a space for reflection
Provide a space for the government to target its information and services
Key features of our online community:
it is by invitation only
People do not know each other, and we do not know who they are.
Cécile Paris | Page 2

3. Encouraging interactions
Activities encouraging reflection and discussions
Resources (toolkit and multimedia)
Discussion forum
“Friends circle”
Gamification elements
Live Chat
Cécile Paris | Page 3

4. Social Trust Model We define social trust as
“the positive behaviour/interactions of users in the community at any particular time in a certain context”
Three important aspects of this definition:
User Behaviour
Temporal factor
Context
Source:
Cécile Paris | Page 4

5. STrust: A behaviour based social trust model [TRUM 2011]
Three types of trust are distinguished:
Popularity Trust (PopTrust)
Engagement Trust (EngTrust)
Social Trust (Strust)
Two types of behaviour:
Active
Passive
Two types of nodes:
Active
Passive

6. An Example – Social Behaviour Representation
Cécile Paris | Page 6

7. An Example from our Community
Cécile Paris | Page 7

8. SRec: What do we need?
Cécile Paris | Page 8

9. SREC: A Recommender System based on STrust
Cécile Paris | Page 9

10. Context & Activities Weights
Recommender is tuned by adjusting a weight given to context specific interactions
Cécile Paris | Page 10

11. Recommendations from SRec
People
Content
Cécile Paris | Page 11

12. Related Work Recommendation Systems Social Behaviour Features
Content-based - similarity with the items consumed by the users
Collaborative Filtering – items consumed by the people with similar taste or preference
Trust based – using trust network
Hybrid – a combination of above
Social Behaviour
Based on social behaviour
Interactions (with people and content) as well as rating
Features
Single model for both content and people
Context-aware (e.g., based on forum discussions) – fine granularity

13. Discussion and Future Work
Social behaviour based recommendations
“Stranger” environment
Based on social trust
Single model for both content and people
Context-aware (e.g., based on forum discussions)
Different roles for the people (e.g., leaders, mentors, etc.)
Based on both interactions and ratings
Future work
Analysis of the results from our online community
Comparison with existing recommendation systems
Is it really a unified model?
Can we tune the model to produce content-based, collaborative filtering and trust-based recommender?
Need further investigation and work.

14. Recommending from the same Social Trust model: People and resources
People: Different roles:
Leaders Mentors Friends
Resources: different types
Acknowledgments:
Nathalie Colineau
Brian Jin
(Adam Strickland)
Bo Yan
Payam Aghaei Pour
Cécile Paris | Page 14