SREC: A Social Behaviour Based Recommender for Online Communities

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Presentation transcript:

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

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

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

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: http://www.manchester.ac.uk/research/mbs/Alistair.sutcliffe/ Cécile Paris | Page 4

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

An Example – Social Behaviour Representation Cécile Paris | Page 6

An Example from our Community Cécile Paris | Page 7

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

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

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

Recommendations from SRec People Content Cécile Paris | Page 11

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

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.

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