Urban Point-of-Interest Recommendation by
Mining User Check-in Behaviors
Josh Jia-Ching Ying, Eric Hsueh-Chan Lu, Wen-Ning Kuo and Vincent S. Tseng
Institute of Computer Science and Information Engineering
National Cheng Kung University
No.1, University Road, Tainan City 701, Taiwan (R.O.C.)
Intelligent DataBase
System Lab, NCKU, Taiwan
Outline
 Introduction
 Background
 Motivation
 Challenges
 Proposed Method – UPOI-Mine
 Experimental Results
 Conclusions
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Introduction – Background
 The markets of Location-Based Services (LBSs) in
urban areas have grown rapidly.
 Effective and efficient urban POI recommendation
techniques are desirable.
 Location Based Social Network (LBSN) data is widely
used for building POI recommendation model.
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Introduction – Background (cont.)
 heterogeneous data
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Introduction – Motivation
We can not accurately catch users’ preference by
analyzing his and his friend’s check-in actives
?
5
?
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Introduction – Challenges
 How to understand user preference from LBSN data?
 How to extract useful features from heterogeneous data?
 How to precisely estimate the relevance between a user-
POI pair based on the extracted features?
 How to integrate heterogeneous information?
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Proposed Method – UPOI-Mine
Offline :UPOI-Mine
Individual
Preference (IP)
POI popularity
(PP)
Social Factor
(SF)
Location
Types
Check-in
Data
Social
Links
LBSN Dataset
Online: Recommender
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Feature Extraction
Offline :UPOI-Mine
Individual
Preference (IP)
POI popularity
(PP)
Social Factor
(SF)
Location
Types
Check-in
Data
Social
Links
LBSN Dataset
Online: Recommender
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Social Factor (SF)
Weighted summation:
Weight
F
SF(useri ,POI j )  [ Interestk,j  Relationi,k ]
k 1
Relationi,k  w CheckSimi,k  (1  w)  DisSimi ,k
Interestk , j 
checkink , j
|S |
 checkin
s 1
k ,s
F: friends of user i
S: the set of POIs
U: the set of user i’s friends
Check-in k,* = check-ins of user k at POI*
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Social Factor – Relation
 Check-in Similarity (CheckSim)
 based on their check-in log
 Relative Distance Similarity (DisSim)
 based on their geographic distance
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Relation – CheckSim
CheckSimi,j 
(1 0)  (0 10)  (2  0)  (5 1)  (0  0)
1  0  2  5  0  0  10  0  1  0
2
2
2
2
2
2
2
2
2
2
 0.0908
Friend Indicator
i
11
i
j
k
…
POI ID
A
B
C
D
E
0
1
0
…
user i
1
0
2
5
0
user j
0
10
0
1
0
user k
1
1
0
0
0
j
1
0
1
…
k
0
1
0
..
user l
1
1
1
1
1
…
…
…
…
…
…
…
…
…
…
…
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Relation – DisSim
 Distance  dissimilarity
 Maxi=1000
Distance
Friend Indicator
i
j
k
…
i
j
k
…
i
0
100
10
…
i
0
1
0
…
j
100
0
50
…
j
1
0
1
…
k
10
50
0
..
k
0
1
0
..
…
…
…
…
…
…
…
…
…
…
100
DisSim i,j  1 
 0.9
1000
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Social Factor – Example
w  0.1
10
Social Fact orfrom User B 
 [0.1 0.5  (1  0.1)  0.03]  0.0077
100
Social Factor from User C  .....
 Social Factor from User D  .....
Social Factor from User E  .....
-------------------------------social factor of user A to POI k
User A
Relation:
CheckSim(A, B) = 0.5
DisSim(A, B) = 0.03
?
POIk
User B
#Check-ins at POIK : 10
#Total Check-ins : 100
Interest(B, POIK) =
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10
100
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Individual Preference (IP)
highlight
• Individual Preference(IP)
• HPrefi,h
• CPrefi,c
category
IP(useri ,POI j ) 



HCounth, j 
   C Pr efi,c  I ctg( c ) (POIj )   (1   )    H Pr efi,h 

HCountg , j 
cC
hH 

gH


, where I(s,c) is an indicatorfunctiondefined as
1
I ctg( c ) (POIj )  
0
14
if POIj  ctg(c)
otherwise
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Individual Preference – HPref & CPref
POI
A(c1)
B(c2)
C(c2)
D(c3)
Highlight
h1,h2
h1,h2
h2
h3
Check-in count
5
1
2
2
User
c1
c2
c3
User1
A
B,C
D
10
h1
h2
h3
User1
A,B
A,B,C
D
Total
5+1
5+1+2
2
c4
5 1 5 1 2
16
16
5 1 2 2
10 10 10
15
Total
Category
CPrefi,c
Highlight
HPrefi,h
C1
0.5
H1
0.375
C2
0.3
H2
0.5
C3
0.2
H3
0.125
h4
h5
Total
0
0
16
2
16
proportion of
check-ins of
the label
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Individual Preference – Example
 There is only one category for one POI.
 There are many highlights for one POI.
Counts of highlight
POI
Category: Hotdog & Sausages
Highlight: Coffee(12), Cheese(88)
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User A’s pref table
Category
CPref
Seafood
0.5
Hotdog &
Sausages
0.1
Fast food
0.1
Steak
0.3
Highlight
HPref
Coffee
0.5
Sightseeing
0.1
Ice Cream
0.1
Cheese
0.3
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Individual Preference – Example (cont.)
User A’s pref table
Category
CPref
Seafood
0.5
Hotdog &
Sausages
0.1
Fast food
0.1
IP( UserA, POI j )  0.2  0.1 
Steak
0.3
12
88 

(1  0.2)  (0.5 
)  (0.1
)
100
100 

HPref
 0.168
Highlight
HPref
Coffee
0.5
Sightseeing
0.1
Ice Cream
0.1
Cheese
0.3
POI A
Category: Hotdog & Sausages
Highlight: Coffee(12), Cheese(88)
  0.2
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CPref
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POI Popularity (PP)
 POI Popularity
 Relative Popularity of POI
 Normalized based on category
RPj 
checkinsj
 checkins
POI k CS
k
, where CS is theset of P OIs which in thesame category with P OI j.
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POI Popularity – Example
Frank
Category: Hot Dogs
RPFrank
19
Hot Dogs
Check-in count
Frank
4,032
KKK
25
……
…
total
100,000
4,032

 0.04032
100,000
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Relevance Estimation
Offline :UPOI-Mine
Individual
Preference (IP)
POI popularity
(PP)
Social Factor
(SF)
Location
Types
Check-in
Data
Social
Links
LBSN Dataset
Online: Recommender
20
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Relevance Estimation – Example
To estimate the relevance of each pair of user to POI,
Target
we use these feature to learn a Regression-Tree Model.
User ID
POI ID
SF
PP
IP
Relevance
1
A
0.2
0.1
0.001
3
1
B
0.05
0.2
0.1
5
1
C
0.004
0.1
0.9
1
…
…
…
…
…
…
N
D
0.5
0.15
0.06
2
Regression-Tree Model
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Relevance Estimation – Regression-Tree
Model
 Regression-Tree Model has shown excellent performance
for numerical value prediction
• Demographic Prediction
• Bio Life Cycle Analysis
• Prediction of Geographical Natural
 Learning Steps:
 1. Building the initial tree
 2. Linear regression model for each leaf node
 3. Pruning the tree
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Recommender
Offline :UPOI-Mine
Individual
Preference (IP)
POI popularity
(PP)
Social Factor
(SF)
Location
Types
Check-in
Data
Social
Links
LBSN Dataset
Online: Recommender
23
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Experimental Evaluation
 Experimental dataset – Gowalla Dataset
 Near or within New York City
 1,964,919 POIs
 18,159 people
 5,341,191 Check-ins
 392,246 Friendship Links
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Experimental Evaluation
 Experimental measurements
 Normalized Discounted Cumulative Gain (NDCG)
if i  1
G[i ],
 DCG[i  1]  G[i ],
if i  b

DCG[i ]  
 DCG[i  1]  G[i ] , if i  b

logb i
NDCG @ p 
DCG[ p]
IDCG[ p]
 To measure ranking performance of relevance score of top k POIs in
recommendation list
 Mean Absolute Error (MAE)
1 n
MAE   f i  yi
n i 1
 To measure error of relevance score of all POIs
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Experimental Evaluation (cont.)
 Ground Truth
x  avg

3

 2, if x  avg

 max -avg

x-avg
3  2, if x  avg

 min -avg
avg = 200
POI ID
Check-in
Relevance
A
50
1
B
50
1
C
500
5
D
200
3
 Baseline
 Trust Walker
 M. Jamali, M. Ester. TrustWalker: A Random Walk Model for Combining
Trust-based and Item-based Recommendation. Proceedings of KDD,
pages 397-406, Paris, 2009.
 Multi-Factor CF-based
 M. Ye, P. Yin, W.-C. Lee and Dik-Lun Lee. Exploiting Geographical
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Influence for Collaborative Point-of-Interest Recommendation.
Proceedings of SIGIR, pages 1046-1054, Beijing, China, 2011.
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Comparison of Various Features
 The Individual Preference is more important than Social Factor for urban
MAE
0.7
0.62
0.6
0.5
27
0.63
0.65
0.59
NDCG@10
POI recommendation.
100%
80%
60%
40%
20%
0%
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MAE
0.7
0.6
0.5
28
NDCG@10
Comparison of Various Features (cont.)
100%
80%
60%
40%
20%
0%
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Comparison with Existing
Recommenders
NDCG@10
TrustWalker
Multi-Factor CF-based
Multi-Factor CF-based(geographic influence)
Multi-Factor CF-based( user prefrence influence)
Multi-Factor CF-based( social influence)
Our approach (PP)
Our approach (SF)
Our approach (IP)
Our approach (All)
0% 10% 20% 30% 40% 50% 60% 70% 80% 90%
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Comparison with Existing
Recommenders (cont.)
MAE
TrustWalker
Multi-Factor CF-based
Multi-Factor CF-based(geographic influence)
Multi-Factor CF-based( user prefrence influence)
Multi-Factor CF-based( social influence)
Our approach (PP)
Our approach (SF)
Our approach (IP)
Our approach (All)
0.00
30
0.50
1.00
1.50
2.00
2.50
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Conclusions
 We proposed a novel urban POIs recommendation which is
called UPOI-Mine by mining users’ preferences.
 we propose three kinds of useful features
 Social Factor
 Individual Preference
 POI Popularity
 Through a series of experiments by the real dataset Gowalla
 We have validated our proposed UPOI-Mine and shown that
UPOI-Mine has excellent performance under various
conditions.
 The Individual Preference is more important than Social
Factor for urban POI recommendation.
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Thank you for your attentions
Question?
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