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Multi-Layer Friendship Modeling for Location-Based Mobile Social Networks
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Multi-Layer Friendship Modeling for Location-Based Mobile Social Networks

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Presented at MobiQuitous, Toronto, Canada, July 2009.

Presented at MobiQuitous, Toronto, Canada, July 2009.


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  • 1. Multi-Layered Friendship Modeling for Location-based Mobile Social Networks Nan Li and Guanling Chen Department of Computer Science, University of Massachusetts Lowell July 14, 2009 Toronto, Canada MobiQuitous 2009
  • 2. Online Social Network Success
    • Popular (half billion ww users)
    • Sticky (26m per day)
  • 3. OSN Goes Mobile
    • Already top Web destinations on smartphones
    • Unique feature – location
      • GPS-enabled phones
      • Sharing current location
      • Attaching location to user-generated content
    • Outlook
      • LSN >$3.3B revenue by 2013 (ABI)
    • Dodgeball, Loopt, Brightkite, Whrrl Google Latitude, Foursquare
  • 4. Brightkite
    • Startup founded 2005, Denver CO
      • Angel funding $1M, 03/2008
      • Private beta, 04/2008
      • Opened to public, 10/2008
    • User activity
      • Check in, status update, photo upload
      • All attached with current location
      • Updates through SMS, Email, Web, iPhone…
    • Social graph with mutual connection
      • See your friends’ or local activity streams
  • 5. Usage Snapshot
  • 6. Contributions
    • Data collection from Brightkite
      • 19k users; 1.5m updates
    • Quantitative correlation model for friendship
      • User tags, social graph, location/activity
    • Evaluation using 10m training data and 45d test data
      • Outperformed than Naïve Bayes classifier or J48 decision tree algorithms
  • 7. Data Collection
    • Brightkite Web APIs
    • 12/9/08-1/9/09: 18,951 active users
    • Back traced to 3/21/08: 1,505,874 updates
    • Profile: age, gender, tags , friends list
    • Social graph: 41,014 nodes and 46,172 links
    • Testing data: next 45 days had 5,098 new links added
  • 8.  
  • 9. Tag Cloud
  • 10. Basic Approach
    • Coming up metrics that
      • Differentiate friends and non-friends
      • Tags, social graph, location, activities
    • Combination of the metrics
    • Training and testing with traces
  • 11. Using Metrics
  • 12. Metric Combination
  • 13. Social Graph
  • 14. Social Graph Metric
  • 15. Tag Graph
    • 1000 most popular tags as the nodes
    • Complete graph
    • Link weight reflects likelihood of two tags shared by friends
  • 16. Tag Graph Metric
  • 17. Location Graph
  • 18. Location Graph Metric
  • 19. Rank Value Result
  • 20. Modeling Accuracy
    • Take another 100,000 non-friend pairs
      • Not in training data
    • Plus the newly added 5,098 friend pairs
    • Sort the prediction values
  • 21. ROC Curve
  • 22. Top Recommendations
  • 23. Information Gain Worldwide buzz: Planetary-scale views on an instant-messaging network. J. Leskovec and E. Horvitz, June 2007.
  • 24. Discussions
    • Model stability as Brightkite grows
      • Does not require frequent re-calculation
    • On-demand recommendation
      • Heuristics to speed up metric calculation
    • Possible improvement
      • Different metrics, or combination methods
    • “ Private” updates
      • Conjectured to be few, but no proof
  • 25. Related Work
    • Industrial solutions: Facebook, Twitter
      • Technical details unknown
    • OSN structural analysis
      • Aggregated behavior not suitable for individual recommendations
    • Collective filtering
      • User-item vs. user-user
  • 26. Conclusion
    • Correlated attribute combination has good friendship recommendation power
      • Interests, social graph, location
    • Location metric is important
      • Gender and age not so much
    • Future work
      • System implementation
      • Real-user action-based evaluation
  • 27. Acknowledgement
    • Anonymous reviewers
    • Shepherd- Sharad Agarwal
    • Best Paper Award committee