Location-Based Social Networks Yu Zheng and Xing Xie Microsoft Research Asia Chapter 8 and 9 of the...
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Location-Based Social Networks Yu Zheng and Xing Xie Microsoft Research Asia Chapter 8 and 9 of the book Computing with Spatial Trajectories
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Transcript of Location-Based Social Networks Yu Zheng and Xing Xie Microsoft Research Asia Chapter 8 and 9 of the...
Location-Based Social Networks
Yu Zheng and Xing Xie
Microsoft Research Asia
Chapter 8 and 9 of the bookComputing with Spatial Trajectories
Outline • Chapter 8 (Location-based social networks: Users)
– Concepts, definition, and research philosophy – Modeling user location history– Computing user similarity based on location history– Friend recommendation and community discovery
• Chapter 9 (Location-based social networks: Locations)– Generic travel recommendations
• Mining interesting locations and travel sequences• Trip planning and itinerary recommendation • Location-activity recommendation
– Personalized travel recommendation• User-based collaborative filtering• Item-based collaborative filtering• Open challenges
Social Networks
“A social network is a social structure made up of individuals connected by one or more specific types of
interdependency, such as friendship, common interests, and shared knowledge.”
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Social Networking Services
A social networking service builds on and reflects the real-life social networks among people through online platforms such as a website, providing ways for users to share ideas, activities, events, and interests over the Internet.
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Locations
Location-acquisition technologiesOutdoor: GPS, GSM, CDMA, …Indoor: Wi-Fi, RFID, supersonic, …
Representation of locationsAbsolute (latitude-longitude coordinates)Relative (100 meters north of the Space Needle) Symbolic (home, office, or shopping mall)
Forms of locationsPoint locationsRegions Trajectories
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Locations + Social Networks
Add a new dimension to social networksGeo-tagged user-generated media: texts, photos, and videos, etc.Recording location history of users
Location is a new object in the networkBridging the gap between the virtual and physical worlds
Sharing real-world experiences onlineConsume online information in the physical world
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Examples
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Physical world
Virtual world
Sharing &Understanding
Generating &Consuming
Inte
racti
ons
Location-Based Social Networks
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Sharing
Understanding
Sharing Geo-tagged mediaVirtual Physical worlds
UnderstandingUser interests/preferencesLocation propertyUser-user, location-location, user-location correlations
Locations
Social networks
Locations An new dimension: Geo-tagAn new object
Social networksExpanding social structuresRecommendations
UsersLocationsmedia
Data + Intelligence
Third Party Services
Microsoft Services
Scenarios - Sharing
Data + Intelligence
Third Party Services
Microsoft Services
Data Information Knowledge Intelligence
Scenarios - Understanding
Location-Based Social Networks (LBSN)
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not only mean adding a location to an existing social network so that people in the social structure can share location-embedded information, but also consists of the new social structure made up of individuals connected by the interdependency derived from their locations in the physical world as well as their location-tagged media content
Here, the physical location consists of the instant location of an individual at a given timestamp and the location history that an individual has accumulated in a certain period. The interdependency includes not only that two persons co-occur in the same physical location or share similar location histories but also the knowledge, e.g., common interests, behavior, and activities, inferred from an individual’s location (history) and location-tagged data.
From Book “Computing With Spatial Trajectories”
Categories of LBSN Services
Geo-tagged-media-based
Point-location-driven
Trajectory-centric
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Geo-
LBSN Services Focus Real-time Information
Geo-tagged-media-based Media Normal Poor
Point-location-driven Point location Instant Normal
Trajectory-centric Trajectory Relatively Slow Rich
Locations
Research Philosophy
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Use
r-L
ocat
ion
Gra
ph
Users
Trajectories
User Graph
User Correlation
Location Graph
Location Correlation
Location-tagged user-generated content
Research Philosophy
SharingMaking sense of the dataEffective and efficient information retrieval……
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Replay
Share
Replay travel experiences on a map with a GPS trajectory
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Research Philosophy
UnderstandingUnderstanding usersUnderstanding locationsUnderstanding events
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User Graph
Location Graph
Understanding Users (Chapter 8)
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User similarity/link prediction
Experts/Influencers detection
Community Discovery
Understanding Locations (Chapter 9)
Generic recommendationMost interesting locations and travel routes/sequencesItinerary planningLocation-activity recommenders
Personalized recommendationLocation recommendations
User-based collaborative filtering modelItem-based collaborative filtering model
Open challenges
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Understanding Events
Anomaly Crowd Behavioral Patterns
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Mining User Similarity Based on Location History
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22Grouping users in terms of the similarity between their location histories, and conduct personalized location recommendations.
GIS ‘08/Trans. On the Web
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GPS trajectories
Geo-Location history
User similarity
Cinema 2 Museum 1
Coffee 3
Semantic Location history
Model user location historyGeographic spacesSemantic spaces
Mining User Similarity Based on Location History
Mining User Similarity Based on Location History
Computing user similarityHierarchical propertiesSequential propertiesPopularity of a location
24Stands for a stay point SStands for a stay point cluster cij
{C }High
Low
Shared Hierarchical Framework
c10
c20 c21
c30 c31 c32 c33 c34
A B2
C4
S1
A B D C
D0.5
E5
E
F2
2 4 0.5 2
1 2 3 4 5
F2
G2
2
6
0.5 1 1
1 0.5 2 3.5 1
S2
7
,
Layer 1
Layer 2
Layer 3
G3
G1
G2
a
e
c
A
B
3. Individual graph building
GPS Logs of User 1
GPS Logs of User 2
GPS Logs of User n
GPS Logs of User i
GPS Logs of User i+1
GPS Logs of User n-1
Stands for a stay point SStands for a stay point cluster cij
{C }High
Low
Shared Hierarchical Framework
c10
c20 c21
c30 c31 c32 c33 c34
Layer 1
Layer 2
Layer 3
G3
G1
G2High
Low
a bd
e
A
B
GPS Logs of User 1
GPS Logs of User 2
1. Stay point detection
2. Hierarchical clustering
Friend and Location Recommendation
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Similar Users Retrieval
User taste inferring
L1, L2, …., Lnu1 u2..
un
x1, x2, …, xny1, y2, …, yn
.
.z1, z2, …, zn
Location Candidates DiscoveringRanking Locations
Mining interesting locations and travel sequences from GPS trajectories
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28Mining interesting locations, travel sequences, and travel experts from user-generated travel routes
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Users: Hub nodes
Locations: Authority nodes
The HITS-based inference model
Location-Activity Recommendation
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Goal: To Answer 2 Typical Questions
A recommended location
Recommended activity list
Recommended location list
Location query
Activity query
Q2: where should I go if I want to do something?
(Location recommendation given activity query)
Q1: what can I do there if I visit some place?
(Activity recommendation given location query)
Problem
Data sparseness (<0.6% entries are filled)
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Activities
Locations5 ? ?
? 1 ?
1 ? 6
Forbidden City
Tourism Exhibition Shopping
Bird’s Nest
Zhongguancun
?
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Solution• Collaborative filtering with collective matrix factorization
– Low rank approximation, by minimizing
where U, V and W are the low-dimensional representations for the locations, activities and location features, respectively. I is an indicatory matrix.
Loc
atio
ns
Features Activities
X = UVTY = UWT Z = VVTU V
Activities
Loc
atio
ns
Act
ivit
ies
Locations
Research Philosophy
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Use
r-L
ocat
ion
Gra
ph
Users
Trajectories
User Graph
User Correlation
Location Graph
Location Correlation
Location-tagged user-generated content
New Challenges in LBSNs
Heterogeneous networksLocations and users Geo-tagged media and trajectories
Special propertiesHierarchy / granularitySequential property
Fast evolving Easy to access a new locationUser experience/knowledge changes
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Conferences
Authors
Papers
Locations
Users
Media
GeoLife Trajectory Dataset (1.1)
Version 1.0 Version 1.1 Incremental
Time span of the collection 04/2007 – 08/2009 04/2007 – 12/2010 +16 months
Number of users 155 167 +12
Number of trajectories 15,854 17,355 +1,501
Number of points 19,304,153 22,294,264 2,990,111
Total distance 600,917 km 1,070,406 km +469,489 km
Total duration 44,776 hour 48,349 hour +3,573 hour
Effective days 8,977 9,694 +717
Transportation mode
Distance (km)
Duration (hour)
Walk 11,457 5,126
Bike 6,335 2,304
Bus 21,931 1,430
Car & taxi 34,127 2,349
Train 74,449 459
Airplane 28,493 37
Other 10,886 335
Total 187,679 12,041
Link to the data
Conferences
ACM SIGSPATIAL Workshop on Location-Based Social Networks LBSN 2011: Nov. 1, 2011, in Chicago (3rd year)Over 40 attendees this year26 submissions. 10 full papers and 4 short papers
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Summary
Locations and social networksSharing and understandingNew challenges and new opportunities
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