Multi-Layered Friendship Modeling for Location-based Mobile Social Networks Nan Li and Guanling Chen Department of Compute...
Online Social Network Success <ul><li>Popular (half billion ww users) </li></ul><ul><li>Sticky (26m per day) </li></ul>
OSN Goes Mobile <ul><li>Already top Web destinations on smartphones </li></ul><ul><li>Unique feature – location </li></ul>...
Brightkite <ul><li>Startup founded 2005, Denver CO </li></ul><ul><ul><li>Angel funding $1M, 03/2008 </li></ul></ul><ul><ul...
Usage Snapshot
Contributions <ul><li>Data collection from Brightkite </li></ul><ul><ul><li>19k users; 1.5m updates </li></ul></ul><ul><li...
Data Collection <ul><li>Brightkite Web APIs </li></ul><ul><li>12/9/08-1/9/09:  18,951  active users </li></ul><ul><li>Back...
 
Tag Cloud
Basic Approach <ul><li>Coming up metrics that </li></ul><ul><ul><li>Differentiate friends and non-friends </li></ul></ul><...
Using Metrics
Metric Combination
Social Graph
Social Graph Metric
Tag Graph <ul><li>1000 most popular tags as the nodes </li></ul><ul><li>Complete graph </li></ul><ul><li>Link weight refle...
Tag Graph Metric
Location Graph
Location Graph Metric
Rank Value Result
Modeling Accuracy <ul><li>Take another 100,000 non-friend pairs </li></ul><ul><ul><li>Not in training data </li></ul></ul>...
ROC Curve
Top Recommendations
Information Gain Worldwide buzz: Planetary-scale views on an instant-messaging network. J. Leskovec and E. Horvitz, June 2...
Discussions <ul><li>Model stability as Brightkite grows </li></ul><ul><ul><li>Does not require frequent re-calculation </l...
Related Work <ul><li>Industrial solutions: Facebook, Twitter </li></ul><ul><ul><li>Technical details unknown </li></ul></u...
Conclusion <ul><li>Correlated attribute combination has good friendship recommendation power </li></ul><ul><ul><li>Interes...
Acknowledgement <ul><li>Anonymous reviewers </li></ul><ul><li>Shepherd- Sharad Agarwal </li></ul><ul><li>Best Paper Award ...
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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.

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

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