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Group Recommendation System for Facebook: Enhancing Online Social Networking

This study explores the development of a group recommendation system for Facebook, focusing on profile analysis, similarity inference, clustering coefficient, and decision tree algorithm. The research emphasizes the importance of user-driven content and finding suitable groups to share common values. By extracting profile features, classifying users, and building decision trees, the system offers personalized group suggestions based on factors like age, gender, interests, and affiliations. Through statistical analysis and feature selection adjustments, the study aims to improve the quality of service on social networking platforms, addressing data cleaning challenges and optimizing decision-making processes for enhanced user experience.

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Group Recommendation System for Facebook: Enhancing Online Social Networking

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Presentation Transcript

  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. After Data Cleaning • Fair representation of group profile • Groups must have at least 10 members • Reduction • Users from 1,580 to 1,023 • Group from 17 to 7

  11. Result 1 • Data set • Training: 75% • Testing: 25% • Accuracy calculation • 25 fold test • Accuracy • 27%

  12. Statistical Analysis: Mean

  13. Statistical Analysis: STD

  14. Adjustment in Feature Selection • Feature score calculation • Using group profile: FSGP • Using group closeness: FSGC • Combination of FSGP and FSGC: FSPC

  15. FSGP vs Accuracy

  16. FSGCvs Accuracy

  17. FSPCvs Accuracy

  18. Result 2

  19. Conclusion • Improving QoS of Online Social Networking • Architecture • Hierarchical clustering • Threshold value to reduce noise • Decision tree • Result poor performance cause • Decision tree: decision boundaries || to coord. • Data overlapping • More work on data cleaning • Feature reduction • From 12 to 2

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