wang-chien lee pervasive data access ( i pda ) group pennsylvania state university wlee@cse.psu.edu
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Wang-Chien LeePervasive Data Access (iPDA) Group
Pennsylvania State University
wlee@cse.psu.edu
Mining Social Network Big DataIntelligent
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Research Dimensions
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IntelligentPervasive Data
Access
Networks
MobilityData
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Research Agenda
Location-Based Services Road/Transportation Networks
Sensor Data Management Peer-to-Peer Data Management Wireless Data Broadcast and
Mobile Access Social Networks
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Developing data management techniques for supporting complex services in networking and mobile environments
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Big Data Landscape
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Social Media
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Location-based Social Networks
Important Aspacts Users (Social Network) Places (Locations) Who visits Where in form of
check-in & trajectory logs
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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.
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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
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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.
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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
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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.
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User preference
Social Influence
Geo Influence DB
POI Recommendation System
Check in
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Recommendation based on user preference i.e., Pure collaborative filtering (CF) approach User-POI matrix
User Preference
POI1 POI2 POI3 POI4 POI5
User1 X X X
User2 X X
User3 X X
User4 X X X
User5 X X
Users with similar preference
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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
POI1 POI2 POI3 POI4 POI5
User1 X X X
User2 X X
User4 X X X
POI1 POI2 POI3 POI4 POI5
User1 X X X
User2 X X
User3 X X
User4 X X X
User5 X X X
user1
user2
user3
user4
user5
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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.
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Phenomenon of spatial clustering in user’s check-ins
Geographical Influence
Let p1 and p2 denote two POIs, and d(p1,p2) be their distance, the probability is denoted by Pr[d(p1,p2)] How likely are two of a user’s check-in POIs in a given distance?
Power law
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Exploiting Geographical Influence for Recommendation
Geographical Influence
User I’s check-in history Pi={p1,p2…}
Which POI is the best candidate to explore?
p1 p2
p3 p4
p5
User iq1
q2
q3
Pr[q1|Pi] = ?Pr[q2|Pi] = ?Pr[q3|Pi] = ?
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Fusion Framework
User’s own preference
Social influence
Geographical influence
q1 (Su)
q2 (Ss)
q3 (Sg)
Fusion
q3q3 q2
q3 q1
q1 q2
q1 (S)
q2
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Tags can support:1) Location search2) Recommendation service3) Data cleaning4) …
32.00%
68.00%
Places missing tagsPlaces 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
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Problem Description
Given a database of user check-in logs <who, where, when> 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.
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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 t1
Binary classifier for tag t2
Binary classifier for tag tm
Decision for t1
Decision for t2
Decision for tm
Classification Process:
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What are the explicit patterns associated with individual places?
Explicit Patterns (EP) Extraction
EP Feature List
Total number of check-in
Total number of unique visitors
Maximum number of check-in of a single userDaily probability of check-in
Hourly probability of check-in
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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 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 Day 8
Bars Bars
Bars
RestaurantRestaurant Restaurant
Restaurant
Restaurant
Restaurant
Restaurant Restaurant Restaurant
Restaurant
Shopping ShoppingShopping
Gym Health Beauty
Spa
?
Check-in log of a user.
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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
Times Places
RandomWalkwith
Restart
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Label Propagation on NRPIR features:Tag 1 – score1Tag 2 – score2….Tag k – scorek
restaurant
restaurant
shopping
?
restaurant
restaurant
shopping
Label propagation
Restaurant 0.66Shopping 0.34
restaurant
restaurant
shopping
restaurant
restaurant
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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
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