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

    • 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
    • Online Social Network Success
      • Popular (half billion ww users)
      • Sticky (26m per day)
    • 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
    • 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
    • Usage Snapshot
    • 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
    • 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
    •  
    • Tag Cloud
    • 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
    • Using Metrics
    • Metric Combination
    • Social Graph
    • Social Graph Metric
    • Tag Graph
      • 1000 most popular tags as the nodes
      • Complete graph
      • Link weight reflects likelihood of two tags shared by friends
    • Tag Graph Metric
    • Location Graph
    • Location Graph Metric
    • Rank Value Result
    • 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
    • ROC Curve
    • Top Recommendations
    • Information Gain Worldwide buzz: Planetary-scale views on an instant-messaging network. J. Leskovec and E. Horvitz, June 2007.
    • 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
    • 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
    • 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
    • Acknowledgement
      • Anonymous reviewers
      • Shepherd- Sharad Agarwal
      • Best Paper Award committee