1. Wang-Chien Lee i Pervasive Data Access ( i PDA) Group Pennsylvania State University wlee@cse.psu.edu Mining Social Network Big Data Intelligent

2. Research Dimensions Industry Day Intelligent Pervasive Data Access Networks Mobility Data 4/3/14 2

3. Research Agenda Location-Based Services Road/Transportation Networks Sensor Data Management Peer-to-Peer Data Management Wireless Data Broadcast and Mobile Access Social Networks Industry Day Developing data management techniques for supporting complex services in networking and mobile environments 4/3/14 3

4. Industry Day Big Data Landscape 4/3/14 4

5. Social Media 4/3/14Industry Day 5

6. Location-based Social Networks Important Aspacts Users (Social Network) Places (Locations) Who visits Where in form of check-in & trajectory logs 4/3/14Industry Day 6

7. LBSN App.’s & Research Opp.’s LBSN users can track & share their locations and relevant info. Collective social intelligence can be leveraged from user- generated location data to enable novel applications. LBSN Applications Suggesting the best restaurants, finding popular hiking routes, or forming a biking community. Recommendation services for location, activity, trip planning, friends, etc. Research opportunities Techniques for LBSN Apps, social network analysis, user profiling, data management and mining, pervasive computing, etc, are urgently needed. 4/3/14Industry Day 7

8. Point-of-Interest Recommendation POI Recommendation Helps a user to explore new POIs Good for local business to gain customers Where to have dinner tonight? Requirements Interests, e.g., Seafood Geo-proximity, e.g,, not too far away Real-time, i.e., time is money 4/3/14Industry Day 8

9. Collaborative Filtering Treating POI as items The idea is that users’ preference can be deduced by other users who exhibit similar visiting behaviors to POIs in previous check-in activities Key issue is to find similar users and similar places/POIs effectively and efficiently. 4/3/14Industry Day 9

10. Social & Geo Influences POI recommendation in LBSN is more than a problem of item recommendation Social Network People may turn to friends for suggestion Geographical Proximity Tobler’s First law of geography “Everything is related to everything else, but near things are more related than distant things” People may go to places near  home or office  favored places 4/3/14Industry Day 10

11. Our approach Incorporate the following three factors: User preference Social Influence from friends who has a role on user activities. Geographical influence existing in user activities. 4/3/14Industry Day User preference Social Influence Geo Influence DB POI Recommendation System Check in 11

12. Recommendation based on user preference i.e., Pure collaborative filtering (CF) approach User-POI matrix User Preference Users with similar preference 4/3/14Industry Day 12

13. Recommendation based on Social influence Social influenced CF approach  Similarity function considers both the strength of social tie and check-in similarity … Friend-POI matrix Social Influence user1 user2 user3 user4 user5 4/3/14Industry Day 13

14. Social Influence Selection Model Social Influence Selection Model User u picks a friend (f) which includes herself (i.e., f=u). Social influence. User f generates a latent topic z. User preference. Latent topic z generates item i and a descriptive word w. Nov. 2013 14 Industry Day

15. Phenomenon of spatial clustering in user’s check-ins Geographical Influence Let p 1 and p 2 denote two POIs, and d(p 1,p 2 ) be their distance, the probability is denoted by Pr[d(p 1,p 2 )] How likely are two of a user’s check-in POIs in a given distance? Power law 4/3/14Industry Day 15

16. Exploiting Geographical Influence for Recommendation Geographical Influence User I’s check-in history P i ={p 1,p 2 …} Which POI is the best candidate to explore? p1p1 p2p2 p3p3 p4p4 p5p5 User i q1q1 q2q2 q3q3 Pr[q 1 |P i ] = ? Pr[q 2 |P i ] = ? Pr[q 3 |P i ] = ? 4/3/14Industry Day 16

17. Fusion Framework User’s own preference Social influence Geographical influence q 1 (S u ) q 2 (S s ) q 3 (S g ) Fusion q3q3 q3q3 q2q2 q3q3 q1q1 q1q1 q2q2 q 1 (S) q2q2 4/3/14Industry Day 17

18. Tags can support: 1)Location search 2)Recommendation service 3)Data cleaning 4)… Places missing tags Places with tags The above shows statistics summarized from our dataset collected from Whrrl. Statistics in our Foursquare dataset is similar. Semantic Annotation of Places Tags are very useful! Tags are missing 4/3/14Industry Day 18

19. Problem Description Given a database of user check-in logs where some places are tagged, infer tags for the rest of places i.e., places with question mark in the above figure How to automatically label appropriate tags on places is a very challenging issue! Our approach is to reduce the place semantic annotation problem into a classification problem. 4/3/14Industry Day 19

20. How to learn the classifier for a tag (or tag type)? Feature extraction is very important Features explicitly describing places Features implicitly correlating similar places (i.e., places with same/similar tags) Feature source? The SAP Framework Feature Extraction Component Check-in logs Place Binary classifier for tag t 1 Binary classifier for tag t 2 Binary classifier for tag t m Decision for t 1 Decision for t 2 Decision for t m Classification Process: check-in logs Industry Day4/3/14 20

21. What are the explicit patterns associated with individual places? Explicit Patterns (EP) Extraction 4/3/14Industry Day 21

22. Are places really correlated? If yes, how do we extract the IR between places? Places checked in by the user at around the same time are probably in the same category Implicit Relatedness (IR) Extraction 00:00 23:59 Day 1Day 2Day 3Day 4Day 5Day 6Day 7Day 8 Bars Restaurant Shopping Gym Health Beauty Spa ? Check-in log of a user. Industry Day4/3/14 22

23. Build an NRP by exploring the regularities in users- places and time-places interactions. Network of Related Places (NRP) Relatedness between places Network of Related Places (NRP) Users Places TimesPlaces Random Walk with Restart Random Walk with Restart 4/3/14Industry Day 23

24. Label Propagation on NRP IR features: Tag 1 – score1 Tag 2 – score2 …. Tag k – scorek restaurant shopping ? ? restaurant shopping Label propagation Restaurant 0.66 Shopping 0.34 Restaurant 0.66 Shopping 0.34 restaurant shopping restaurant 4/3/14Industry Day 24

25. LBSNs have received a lot of attention from the research community LBSN data have rich social and location information. Novel applications can be developed from the rich user-generated data in LBSNs. We have incorporated social and geo influences with collaborative filtering technique for POI recommendation. To address the semantic annotation problem in LBSNs, we extract explicit pattern (EP) of individual places and implicit relatedness (IR) among places to classify the missing tags. New applications and more research are forth coming. Conclusion 4/3/14Industry Day 25

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