Tony Jebara's Presentation at eComm 2009

Mobile Phones Reveal the Behavior of Places and People Tony Jebara 2

Tony Jebara www A network of online places 3

Tony Jebara facebook A network of online people 4

Tony Jebara From online to real networks? What’s next? a network of real places a network of real people Online data is easy to get, what about the real world? 5

Tony Jebara From online to real networks? What’s next? a network of real places GPS a network of real people Online data is easy to get, what about the real world? 6

Tony Jebara GPS and location data Collaborative Marketing Advertising Search Social Filtering Recommendation SENSE NETWORKS & COLUMBIA MACHINE LEARNING ANALYSIS, CITYSENSE, NETWORKS OF PLACES & PEOPLE GPS VEHICLES APPS MAPS CARRIERS 7

DJIA breaks 1y low DJIA all time high 14k

Volatility Just Before Market Peak How late People Going Out Later Than Ever Nightlife volume How late New Years New Years Nightlife volume

Tony Jebara CitySense: where is everyone •Citysense: real-time density of users at every street corner •Poisson models find most active bars/restaurants 11

Tony Jebara Next: where’s everyone like me Need to personalize by building a network of people Each color is a different socialcluster or tribe 12

Tony Jebara Network of People who is like whom? 13

Tony Jebara Network of People Hard to say if User A is like User B… User A User B … don’t just see if they physically … where do they overlap semantically (network of places) 14

Tony Jebara Network of Places Is place A like place B? Look at each place’s Flow, Commerce & Demographics 15

Tony Jebara Network of Places: Commerce Get each block’s SIC (standard industrial categorization) code & cluster 17

Tony Jebara Network of Places: Demographics Get each block’s census demographic data & cluster 18

Tony Jebara Encoding people For each user, convert GPS trail into matrix of probabilities for week hour probability of being in 1) flow cluster 2) sic cluster 3) demographic cluster Week FLO FLO … FLO2 SIC SIC … SIC DEM DEM … DEM Hour 1 2 0 1 2 97 1 2 78 1 .03 .31 .14 .03 .05 .41 .11 .04 .01 2 .14 .34 .02 .04 .05 .52 .01 .01 .00 … 168 .07 .34 .51 .02 .06 .48 .02 .01 .00 19

Tony Jebara Encoding people 9 example users compute pair-wise similarity from weekly exposure matrices 20

Tony Jebara Network of People Churn Advertising Marketing Collaborative Filtering Demographics 21

Tony Jebara Network of People Tribes How often do they go out each day of the week? Where do they hang out? What is the avg age of most people in the neighborhoods they spend time in? How racially diverse are the neighborhoods they spend time in? Are the places they spend time in rich neighborhoods or poor neighborhoods? “Young & Edgy” •Out every night in young, racially diverse, low income neighborhoods “Weekend Mole” “Mature Homebody” •Out occasionally on •Rarely goes out, typically weeknights, typically spends nights in mature, middle-aged, Latino, middle- white, higher income income neighborhoods neighborhoods 22

Proprietary & Confidential Network of People Tribes Recommendation & Marketing based on the Network Identifying Active New Car Shoppers High end large cars for Low end small cars for lower wealthy, middle age families income, middle/older age with kids consumers 23

Tony Jebara Network of People Predictions 24

Tony Jebara The Next Net

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Tony Jebara's Presentation at eComm 2009

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