Group Recommendation System for Facebook

1. Enkh-AmgalanBaatarjav JedsadaChartree ThiraphatMeesumrarn Group Recommendation System for Facebook University of North Texas

2. Overview • Evolution of Communication • Online Social Networking (OSN) • Architecture • Profile feature • Profile Analysis • Similarity inference • Clustering coefficient • Decision tree • Conclusion • Traditional medium of communication • Mail, telephone, fax, E-mail, etc. • Key to successful communication • Sharing common value

3. Online Social Networking • User-driven content • Overwhelming number of groups • Finding suitable groups • Sharing a common value • Improving online social network

4. Architecture • Profile feature extraction • Classification engine • Clustering • Building decision tree • Group recommendation

5. Profile Feature • Group profile defined by profile features of users • Time Zone - Age • Gender - Relationship Status • Political View - Activities • Interest - Music • TV shows - Movies • Books - Affiliations • Note counts - Wall counts • Number of Fiends

6. Profile Analysis

7. Similarity Inference • Hierarchical clustering • Normalizing data [0, 1] • Computing distance matrix to calculate similarity among all pairs of members (a) • Finding average distance between all pairs in given two clusters s and r (a) (b)

8. Clustering Coefficient • Ri is the normalized Euclidean distance from the center of member i • Nk is the normalized number of members within distance k from the center

9. Decision Tree • Decision tree algorithm, based on binary recursive partitioning • Splitting rules • Gini, Twoing, Deviance • Tree optimization • Cross-validation (computation intense)

10. Conclusion • Improving QoS of Online Social Networking • Architecture • Hierarchical clustering • Threshold value to reduce noise • Decision tree • Result