This document summarizes an application of machine learning techniques to location-based social networks. It discusses two applications: 1) GeoSRS, a hybrid social recommender system that provides personalized venue recommendations to users. It extracts data from Foursquare using an API, performs text modeling on tip content, and generates recommendations using both collaborative and content-based approaches. 2) Tweet-SCAN, an event discovery technique that identifies dense groups of geolocated tweets close in space, time, and topic to discover real-world events. It extends the DBSCAN clustering algorithm and represents tweet topics using probabilistic models. The technique is evaluated on tweets from Barcelona events.

1. Applications of Machine Learning
to Location-based Social Networks
Joan Capdevila Pujol
e-mail: jc@ac.upc.edu
website: http://people.ac.upc.edu/jc
Advisors: Jordi Torres Viñals, Jesús Cerquides Bueno

2. 2
Table of contents
Motivation
Location-based Social Networks (LBSNs)
App 1: GeoSRS: A social recommender system
App 2: Tweet-SCAN: An event discovery technique
Conclusions and future trends

3. MOTIVATION
3

4. HOW
FACEBOOK
STARTED
?

5. 5
AS A
“HOT OR NOT”
GAME

6. 6

7. 7
A ML geek might have thought:
“With all this tagged data, I am going to build a classifier to
decide whether the person in the pic is hot or not.”

8. 8
Mark Zuckerberg probably thought:
“I’d rather prefer to keep playing to scale up the network
and then…”

9. 9
1.500 users

10. 10
1.500.000.000 users

11. 11
User engagement through several social networking services:
Linking to friends, colleagues, etc.
Setting school/college
Tagging friends to pictures
Liking publications
Geolocating content
Reviewing business
Expressing how one feels
…

12. 12
User engagement through several social networking services:
Linking to friends, colleagues, etc. à Social graphs
Setting school/college à User profiles
Tagging friends to pictures à Tagged images
Liking publications à Rating information
Geolocating content à Geolocated content
Reviewing business à Textual comments
Expressing how one feels à People feelings
…

13. 13
Community
detection
Content-based
Recommender
Sentiment
Analysis
Image
recognitionTopic
Modeling

14. LOCATION-BASED SOCIAL NETWORKS
(LBSNS)
14

15. Social Networks (SNs)
VIRTUALWORLD
2004 - 2010

16. Location-based Social Networks (LBSNs)
VIRTUALWORLD
Mobile communication + Positioning technologies
PHYSICALWORLD
2010 - …

17. 17
Locations
Location-acquisition technologies
– Outdoor: GPS, GSM, etc.
– Indoor: Wi-Fi, RFID, etc.
Representation of locations
– Absolute (e.g. latitude-longitude coordinates)
– Symbolic (e.g. at Pl. Catalunya, at Aeroport Girona-Costa Brava )
Forms of locations
– Point locations (e.g. Foursquare venues)
– Regions (e.g. Twitter places)
– Trajectories (e.g. Strava)

18. 18
Research lines
Understanding users
– User similarity/link prediction
– Experts/influencers detection
– Community discovery
Understanding locations
– Generic recommendation
• Most interesting locations and travel routes
• Itinerary planning
• Location-activity recommenders
– Personalized recommendation: GeoSRS [Capdevila et al. 2015]
Understanding events
– Anomaly detection: Tweet-SCAN [Capdevila et al. 2015]
– Crowd behavioral patterns
Zheng, Y. 2011

19. 19
Research lines
Understanding users
– User similarity/link prediction
– Experts/influencers detection
– Community discovery
Understanding Locations
– Generic recommendation
• Most interesting locations and travel routes
• Itinerary planning
• Location-activity recommenders
– Personalized recommendation: GeoSRS [Capdevila et al. 2015]
Understanding events
– Anomaly detection: Tweet-SCAN [Capdevila et al. 2015]
– Crowd behavioral patterns
Zheng, Y. 2011

20. 20
Research lines
Understanding users
– User similarity/link prediction
– Experts/influencers detection
– Community discovery
Understanding locations
– Generic recommendation
• Most interesting locations and travel routes
• Itinerary planning
• Location-activity recommenders
– Personalized recommendation: GeoSRS [Capdevila et al. 2015]
Understanding events
– Anomaly detection: Tweet-SCAN [Capdevila et al. 2015]
– Crowd behavioral patterns
Zheng, Y. 2011

21. GEOSRS:
A SOCIAL RECOMMENDER SYSTEM
21
Joan Capdevila, Marta Arias, and Argimiro Arratia. "GeoSRS: A hybrid social
recommender system for geolocated data." Information Systems (2015).

22. Motivation
USERS

23. Motivation
USERS VENUES

24. Motivation
USERS VENUESTIPS

25. Motivation
USERS VENUESTIPS

26. Motivation
USERS VENUESTIPS

27. 27
System block diagram
DATA
EXTRACTION
DATA
PREPROCESSING
TEXT
MODELING RECOMMENDATION MIXER

28. 28
System block diagram
DATA
EXTRACTION
DATA
PREPROCESSING
TEXT
MODELING RECOMMENDATION MIXER

29. 29
Data Extraction

30. 30
Data Extraction
RESTful API

31. 31
Data Extraction
RESTful API
APPs

32. 32
Foursquare API
HTTP METHODS
– GET, POST, PUT ,DELETE
RESOURCES
– Venue, tip, user
e.g. GET https://api.foursquare.com/v2/venues/40a55d80f964a52020f31ee3
ASPECTS
– Tips of a venue, friends of a user
e.g. GET https://api.foursquare.com/v2/venues/40a55d80f964a52020f31ee3/tips
ACTIONS
– Approve a friendship, like a venue
e.g. POST https://api.foursquare.com/v2/venues/40a55d80f964a52020f31ee3/like

33. 33
Foursquare API
App registration https://foursquare.com/developers/apps
Obtain the Foursquare API credentials (Client ID and Client Secret)
Access token
Allows apps to make requests to Foursquare on behalf of a user
Userless request
Specify consumer key’s Client ID and Client Secret
https://api.foursquare.com/v2/venues/search?
ll=40.7,-74&client_id=XX&client_secret=ZZ&v=20151125
Authenticated request
Specify access token
https://api.foursquare.com/v2/users/self/checkins?oauth_token=AA

34. 34
Foursquare API
Technical Limitations
Userless requests to venues/ resource = 5.000 request/hour
Userless requests to other resources = 500 request/hour
Authenticated requests = 500 request/hour*token

35. 35
Foursquare API
Legal Limitations

36. 36
Data Extraction
Goal: extract all tips from venues
in Manhattan (New York)
Medium:
– aspect: venues/VENUE_ID/tips
– resource: venues/search(sw, ne)
Limitations:
– 5000 request/hour
– at most 50 venues per request
SW
NE

37. 37
Quadtree algorithm

38. 38
Quadtree algorithm

39. 39
Quadtree algorithm

40. 40
Quadtree algorithm
In each Quadcell at the tree leaves, there
are at most 50 venues.
Through venues/VENUE_ID/tips, we now
retrieve the tips for this venue
Each tip is linked to a VENUE_ID and
USER_ID
We now have a database of triplets
(USER, TIPS, VENUE) to perform
recommendation

41. 41
Recommendation
Positive Negative
Neutral
ContentSentiment

42. 42
Collaborative Recommendation
Collaborative recommendation based on tips’ sentiment
Positive

43. 43
Content-based Recomendation
Content-based recommendation based on tips’ content
recommend

44. 44
Content-based Recomendation
Content-based recommendation based on tips’ content
Not recommend

45. 45
Hybridization
Simple weighted hybridization technique
Collaborative
Branch
fCOL
Content-based
Branch
fCONT
Hybrid
f: fCOL +α fCONT

46. 46
Evaluation
0
1
2
3
…
Nposk-1

47. 47
Evaluation
0
1
2
3
…
Nposk-1

48. 48
Evaluation
0
1
2
3
…
Nposk-1

49. 49
Results
Evaluation in terms of cumulated density functions (cdf) of the
recommendation error

50. TWEET-SCAN:
AN EVENT DISCOVERY TECHNIQUE
50
Joan Capdevila Jesús Cerquides Jordi Nin Jordi Torres. “Tweet-SCAN: An event
discovery technique for geo-located tweets”. Proceedings of the 18th International
Conference of the Catalan Association for Artificial Intelligence, 2015

51. Motivation

52. 52
Motivation
CAN WE
UNCOVER
PHYSICAL WORLD EVENTS FROM
TWEETS
?

53. 53
Examination of data
We looked at several tweet dimension separately
… from a dataset of tweets collected during “la Mercè” 2014
Spatial Temporal Textual

54. 54
Examination of data
We looked at several tweet dimension separately
… from a dataset of tweets collected during “la Mercè” 2014
Spatial Temporal Textual

55. 55
Examination of data
Spatial and temporal

56. 56
Examination of data
Spatial and temporal

57. 57
Tweet-SCAN
WHAT ABOUT
USING
ALL 3 DIMENSIONS
AT ONCE
?

58. 58
Tweet-SCAN
Tweet-SCAN is a technique to discover events from
geolocated Tweets.
It allows to discover dense groups of Tweets which are close
in space, time and textual meaning.
These dense groups of Tweets are linked to physical world
events
Textual meaning is represented through probabilistic topic
models
Tweet-scan can be seen as an extension of the popular
DBSCAN algorithm or a particular case of GDBSCAN

59. 59
Probabilistic topic modeling
Fig. - Xuriguera et al. 2013
LDA - Blei et al. 2003
HDP - Teh et al. 2006
Latent Dirichlet Allocation (LDA)
Hierarchical Dirichlet Process (HDP)
– Non-parametric version of LDA

60. 60
Probabilistic topic modeling
VAN VAN MARKET - 🚐🚎🍤🍱🍜 Mercat gastronòmada #lepetitbangkok @ Parc de la Ciutadella
http://t.co/5CvnUFoIDa
Topic Proportions: [(1, 0.30002802458675537), (11,0.58330530874655417)]

61. 61
DBSCAN
Density-based Spatial Clustering for Applications with Noise
Noise points
params: Minpts=4, ε =
Ester et al. 1996

62. 62
DBSCAN
Density-based Spatial Clustering for Applications with Noise
Core points
params: Minpts=4, ε =
ε
Ester et al. 1996

63. 63
DBSCAN
Density-based Spatial Clustering for Applications with Noise
Core points
params: Minpts=4, ε =
ε
Ester et al. 1996

64. 64
DBSCAN
Density-based Spatial Clustering for Applications with Noise
Border points
params: Minpts=4, ε =
ε
Ester et al. 1996

65. 65
DBSCAN
Density-based Spatial Clustering for Applications with Noise
Border points
params: Minpts=4, ε =
ε
Ester et al. 1996

66. 66
DBSCAN
Density-based Spatial Clustering for Applications with Noise
ε
Noise point Border point Core point
params: Minpts=4, ε =
Ester et al. 1996

67. 67
Tweet-SCAN
Neighborhood identification
– ε1: spatial (m) – Euclidean distance
– ε2: time (sec) – Euclidean distance
– ε3: text – Jensen-Shannon distance (proper metric for prob. dist.)
Cardinality of the neighborhood
– MinPts – minimum number of neighbors (Tweets)
– µ – minimum percentage of unique users in the neighborhood.

68. 68
Experimentation
We used 45.623 tweets to unsupervisedly discover event-related tweets
by means of Tweet-SCAN.
We seek to understand the parameters role by comparing the resulting
clusters against the 1.163 tagged event-related Tweets.

69. 69
Evaluation
Extrinsic clustering metrics
Amigo et al. 2008
Purity =
Ci
n
max Precision Ci, Lj( )( )
i
∑ → Precision Ci, Lj( )=
Ci ∩ L j
Ci
Precision(C,L)=9/10
Precision(C,L)=1/10
Precision(C,L)=0/10
Precision(C,L)=0/9
Precision(C,L)=8/9
Precision(C,L)=1/9
Precision(C,L)=0/9
Precision(C,L)=0/9
Precision(C,L)=9/9
C C C
L L L

70. 70
Evaluation
Extrinsic clustering metrics
Amigo et al. 2008
Purity =
Ci
n
max Precision Ci, Lj( )( )
i
∑ → Precision Ci, Lj( )=
Ci ∩ L j
Ci
Purity = 0.92
C C C
L L L
Precision(C,L)=9/10
Precision(C,L)=1/10
Precision(C,L)=0/10
Precision(C,L)=0/9
Precision(C,L)=8/9
Precision(C,L)=1/9
Precision(C,L)=0/9
Precision(C,L)=0/9
Precision(C,L)=9/9

71. 71
Evaluation
Extrinsic clustering metrics
Amigo et al. 2008
Purity =
Ci
n
max Precision Ci, Lj( )( )
i
∑ → Precision Ci, Lj( )=
Ci ∩ L j
Ci
Purity = 1

72. 72
Evaluation
Extrinsic clustering metrics
Amigo et al. 2008
Recall(C,L)=9/9
Recall(C,L)=0/9
Recall(C,L)=0/9
Recall(C,L)=1/9
Recall(C,L)=8/9
Recall(C,L)=0/9
Recall(C,L)=0/10
Recall(C,L)=1/10
Recall(C,L)=9/10
InvPurity =
Li
n
max Recall Cj, Li( )( )
i
∑ → Recall Cj, Li( )=
Li ∩C j
Li
C C C
L L L

73. 73
Evaluation
Extrinsic clustering metrics
Amigo et al. 2008
InvPurity =
Li
n
max Recall Cj, Li( )( )
i
∑ → Recall Cj, Li( )=
Li ∩C j
Li
Recall(C,L)=9/9
Recall(C,L)=0/9
Recall(C,L)=0/9
Recall(C,L)=1/9
Recall(C,L)=8/9
Recall(C,L)=0/9
Recall(C,L)=0/10
Recall(C,L)=1/10
Recall(C,L)=9/10
InvPurity = 0.92
C C C
L L L

74. 74
Evaluation
Extrinsic clustering metrics
Amigo et al. 2008
InvPurity =
Li
n
max Recall Cj, Li( )( )
i
∑ → Recall Cj, Li( )=
Li ∩C j
Li
InvPurity = 0.1

75. 75
Evaluation
Extrinsic clustering metrics
Amigo et al. 2008
InvPurity =
Li
n
max Recall Cj, Li( )( )
i
∑ → Recall Cj, Li( )=
Li ∩C j
Li
InvPurity = 1

76. 76
Evaluation
Extrinsic clustering metrics
which is the harmonic mean F(Li,Cj) is the harmonic mean of
Precision(Cj,Li) and Recall(Cj,Li)
Amigo et al. 2008
F =
Li
n
max F Li, Cj( )( )
i
∑ →F Li, Cj( )=
2×Recall(Cj,Li )×Precision( Cj,Li )
Recall(Cj,Li )+ Precision( Cj,Li )

77. 77
Evaluation results
ε1 = 250m, ε2 = 3600s, MinPts = 10, µ = 0.5

78. 78
Evaluation results
ε1 = 250m, ε2 = 3600s, ε3 = 1, MinPts = 10, µ = 0.5

79. 79
Evaluation results
ε1 = 250m, ε2 = 3600s, ε3 = 0.8, MinPts = 10, µ = 0.5

80. 80
Evaluation results
Discovered Events ε3 = 1Tagged Events
Mostra Vins (20-09-2014)
Vanvan market (20-09-2014)

81. 81
Evaluation results
Tagged Events
Mostra Vins (20-09-2014)
Vanvan market (20-09-2014)
Discovered Events ε3 = 0.8

82. 82
Evaluation results
ε1=250m,ε2=3600s
ε1=250m,ε2=1800sε1=500m,ε2=1800s
ε1=500m,ε2=3600s
Fopt = 0.64
ε3 = 1
Fopt = 0.693
ε3 = 0.9
Fopt = 0.62
ε3 = 0.65
Fopt = 0.53
ε3 = 0.9

83. CONCLUSIONS
83

84. Conclusions
The birth of social networks is one of the major causes of
current levels of digitalized personal data.
Social networks have kept the doors opened to the developer
community in order to stimulate the creation of apps.
This “openness” has been materialized with RESTful APIs,
that enables communication between third party apps and
social networks.
Through these APIs we are able to access vast amounts of
data, develop and validate machine learning tools.
However, technical and legal limitations have to be taken
into account to build functional applications.

85. 85
Conclusions
Location-based social networks enable to bridge the virtual
and physical world.
Classical application such as recommender systems have to
be reconsidered to take into account this new dimension.
Recommendation from textual reviews is feasible and
hybridization improves performance.
Data from SN can be very biased by their own services in the
SN (e.g. by their own RS).
Other novel application, such as event discovery, gain
meaning with LBSNs.
Event discovery has to consider textual dimension to uncover
meaningful events

86. FUTURE TRENDS
86

87. 87
Internet of Things
2003
2010
2015
2020
In 2008, the number of things connected to the Internet exceeded
the number of people on earth
M. Swan 2014

88. 88
The Social Internet of Things (SIoT)
Atzori 2012

89. 89
The internet of nano-things
Akyildiz et al. 2010

90. 90
Haplotype social network?

91. 91
References
Zheng, Yu. "Location-based social networks: Users." Computing with Spatial
Trajectories. Springer New York, 2011. 243-276.
Joan Capdevila, Marta Arias, and Argimiro Arratia. "GeoSRS: A hybrid social
recommender system for geolocated data." Information Systems (2015).
Joan Capdevila, Jesús Cerquides Jordi Nin Jordi Torres. “Tweet-SCAN: An
event discovery technique for geo-located tweets”. Proceedings of the 18th
International Conference of the Catalan Association for Artificial Intelligence,
2015
Blei, David M., Andrew Y. Ng, and Michael I. Jordan. "Latent dirichlet
allocation." the Journal of machine Learning research 3 (2003): 993-1022.

92. 92
References
Teh, Yee Whye, et al. "Hierarchical dirichlet processes." Journal of the american
statistical association 101.476 (2006).
Ester, Martin, et al. "A density-based algorithm for discovering clusters in large
spatial databases with noise." Kdd. Vol. 96. No. 34. 1996.
Amigó, Enrique, et al. "A comparison of extrinsic clustering evaluation metrics
based on formal constraints." Information retrieval 12.4 (2009): 461-486.
Melanie Swan. “Quantified Self Ideology. Personal data becomes Big Data”
February 2014. Université Paris Descartes
Akyildiz, Ian F., and Josep Miquel Jornet. "The internet of nano-things." Wireless
Communications, IEEE 17.6 (2010): 58-63.
Luigi Atzori A presentation on THE SOCIAL INTERNET OF THINGS University of
Cagliari, Italy 2012

93. ACKNOWLEDGMENTS:
93
Many Thanks!
Questions?