Measuring Serendipity: Connecting People, Locations and Interests in a Mobile 3G Network Ionut Trestian Supranamaya Ranjan Aleksandar Kuzmanovic Antonio Nucci Northwestern University Narus Inc.

Ionut Trestian Measuring Serendipity: Connecting People, Locations and Interests in a Mobile 3G Network 2 Social network websites among the most popular websites on the Internet Online Social Networks

Ionut Trestian Measuring Serendipity: Connecting People, Locations and Interests in a Mobile 3G Network Mobage Town Japan based mobile social network 11 million users Allows users to: –Send messages, chat in communities, exchange music, read pocket novels, write blogs, play games etc. 3

Ionut Trestian Measuring Serendipity: Connecting People, Locations and Interests in a Mobile 3G Network Loopt 4 Allows contacts to visualize one anothers location using mobile phones and share information Available for Sprint, Verizon, At&t, T-Mobile on devices such as BlackBerry, iPhone and gPhone

Ionut Trestian Measuring Serendipity: Connecting People, Locations and Interests in a Mobile 3G Network Other Location Based Services Sharing your location with friends (BuddyBeacon –for iPhone) Location based searches (EarthComber) Notifications about places and events around you (LightPole) Tagging locations (Metosphere) 5

Ionut Trestian Measuring Serendipity: Connecting People, Locations and Interests in a Mobile 3G Network Research Questions 6 How likely are we to meet in our daily lives people who share common interests in the cyber domain? What is the relationship between mobility properties, location, and application affiliation in the cyber domain? 3,162,818 packet data sessions generated by 281,394 clients in 1196 locations (Base Stations) across a large metropolitan area

Ionut Trestian Measuring Serendipity: Connecting People, Locations and Interests in a Mobile 3G Network Extracting Human Movement 7 1. Intra-session movement RADA Start (contains BSID) RADA Update (contains BSID) 2. Inter-session movement RADA Stop (contains BSID) RADIUS Server Base Station 1 Base Station 2 Note that we have only a sampled view of human movement. How well can we do? Note that we have only a sampled view of human movement. How well can we do?

Ionut Trestian Measuring Serendipity: Connecting People, Locations and Interests in a Mobile 3G Network Extracting Human Movement 8 Despite sampled observations we still do a good job at understanding user movement. The ordering of the curves accounts for the larger time span which can accommodate larger travel distances Despite sampled observations we still do a good job at understanding user movement. The ordering of the curves accounts for the larger time span which can accommodate larger travel distances Most human movement is over short distances.

Ionut Trestian Measuring Serendipity: Connecting People, Locations and Interests in a Mobile 3G Network Extracting Application Interest 9 Dating website Social networking website Music download website InterestKeywords Datingdating, harmony, personals, single, match Musicsong, mp3, audio, music, track, pandora Social netw.facebook, myspace, blog Keyword based URL mining

Ionut Trestian Measuring Serendipity: Connecting People, Locations and Interests in a Mobile 3G Network Rule Definitions 10

Ionut Trestian Measuring Serendipity: Connecting People, Locations and Interests in a Mobile 3G Network Rule Mining 11 Location A Location B (A, B, w, δ) Rule support: Number of people present at A Rule confidence: Number of people that move from A to B Rule confidence probability: confidence/support W δ

Ionut Trestian Measuring Serendipity: Connecting People, Locations and Interests in a Mobile 3G Network 12 Rule Statistics Increase in number of active users at commute hours (8AM and 5PM) Movement rules are more active during day time, also less active during weekend Movement rules are more active during day time, also less active during weekend

Ionut Trestian Measuring Serendipity: Connecting People, Locations and Interests in a Mobile 3G Network Location Rank – Application Accesses 13 Music downloads – anti-correlation with mobility span Mail – correlation with mobility span Social netw. – dominates the medium mobility range

Ionut Trestian Measuring Serendipity: Connecting People, Locations and Interests in a Mobile 3G Network Location Ranking 14 All users spend most of their time in their top 3 locations Comfort zone 3

Ionut Trestian Measuring Serendipity: Connecting People, Locations and Interests in a Mobile 3G Network Location Rank – Application Accesses 15 Music downloads, Dating, Trading heavily accessed in the comfort zone Music downloads, Dating, Trading heavily accessed in the comfort zone Comfort zone Social netw. News and Mail tend to be accessed outside too Social netw. News and Mail tend to be accessed outside too Note that Dating is accessed more in the Comfort Zone

Ionut Trestian Measuring Serendipity: Connecting People, Locations and Interests in a Mobile 3G Network Home vs. Work

Ionut Trestian Measuring Serendipity: Connecting People, Locations and Interests in a Mobile 3G Network Hotspots Via rule mining we detect highly active locations 17 We identify 4 types of such locations –Noon hotspots – 28 such locations Highly active during Noon hours –Night hotspots – 62 such locations Highly active during night hours –Day-office hotspots – 23 such locations Highly active during day hours –Evening hotspots – 8 such locations Highly active during the evening

Ionut Trestian Measuring Serendipity: Connecting People, Locations and Interests in a Mobile 3G Network Biased Application Access at Hotspots 18 Application accesses hotspots Normalized user affiliation Despite similar userbase at hotspots during the seven day interval, application accesses are highly skewed towards certain applications.

Ionut Trestian Measuring Serendipity: Connecting People, Locations and Interests in a Mobile 3G Network Application Access - Time of Day 19 Application accesses non hotspot times Application accesses non hotspots However the bias in application access is not entirely due to an illusive time of day effect ! However the bias in application access is not entirely due to an illusive time of day effect !

Ionut Trestian Measuring Serendipity: Connecting People, Locations and Interests in a Mobile 3G Network 20 Regional analysis – Spectral Clustering Using spectral clustering we: Cluster locations as belonging to regions Cluster users as belonging to regions Spectral clustering doesnt make any assumptions on the shape of the clusters(opposed to k-means)

Ionut Trestian Measuring Serendipity: Connecting People, Locations and Interests in a Mobile 3G Network Clustering Results

Ionut Trestian Measuring Serendipity: Connecting People, Locations and Interests in a Mobile 3G Network Regional Analysis – Research issues Two relevant issues for location based services: –Time independent interactions(useful for tagging services) – part of user trajectories overlap irrespective of the time of the movement –Time dependent interactions – same location same time Questions: –How many distinct people with the same interests do we meet? Strongly dependent on userbase (probability to meet people higher in clusters with bigger userbase) –How often do we meet people? 22

Ionut Trestian Measuring Serendipity: Connecting People, Locations and Interests in a Mobile 3G Network Time Independent Interactions 23 Cluster 1 has a higher number of interactions per location mainly because of larger hotspot density 27/162 (Cluster 1)> 26/257 (Cluster 4) for night hotspots Cluster 1 has a higher number of interactions per location mainly because of larger hotspot density 27/162 (Cluster 1)> 26/257 (Cluster 4) for night hotspots

Ionut Trestian Measuring Serendipity: Connecting People, Locations and Interests in a Mobile 3G Network 24 Who Will Win the Interaction Race? Event typeMobile users Seen in more than 20 locations Static users (hotspot) Spent more than 6 hours in a Hotspot Static users (non-hotspot) Spent more than 6 hours in a non-hotspot Social netw Music Dating Mobile users clearly win the interaction race However it pays off to spend time in popular locations

Ionut Trestian Measuring Serendipity: Connecting People, Locations and Interests in a Mobile 3G Network Conclusions First study at such a large scale aimed at correlating mobility, location, and application usage Provided new insights from user perspective, location perspective, and provider perspective that shows the enormous location based service potential 25