multi-layer friendship modeling for location-based mobile social networks
DESCRIPTION
Presented at MobiQuitous, Toronto, Canada, July 2009.TRANSCRIPT
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Multi-Layered Friendship Modeling for Location-based Mobile
Social NetworksNan Li and Guanling Chen
Department of Computer Science, University of Massachusetts Lowell
July 14, 2009Toronto, Canada
MobiQuitous 2009
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Online Social Network Success• Popular (half billion ww users)• Sticky (26m per day)
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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, WhrrlGoogle Latitude, Foursquare
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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
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Usage Snapshot
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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
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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
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Tag Cloud
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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
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Using Metrics
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Metric Combination
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Social Graph
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Social Graph Metric
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Tag Graph
• 1000 most popular tags as the nodes• Complete graph• Link weight reflects likelihood of two
tags shared by friends
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Tag Graph Metric
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Location Graph
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Location Graph Metric
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Rank Value Result
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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
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ROC Curve
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Top Recommendations
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Information Gain
Worldwide buzz: Planetary-scale views on an instant-messaging network. J. Leskovec and E. Horvitz, June 2007.
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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
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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
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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
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Acknowledgement
• Anonymous reviewers• Shepherd- Sharad Agarwal• Best Paper Award committee