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

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

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, WhrrlGoogle 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