Intelligent DataBase System Lab, NCKU, Taiwan Josh Jia-Ching Ying, Eric Hsueh-Chan Lu, Wen-Ning Kuo and Vincent S. Tseng Institute of Computer Science

Intelligent DataBase System Lab, NCKU, Taiwan

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.)

Urban Point-of-Interest Recommendation by Mining User Check-in Behaviors


Outline

2

Introduction

Background

Motivation

Challenges

Proposed Method – UPOI-Mine

Experimental Results

Conclusions


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.

3


Introduction – Background (cont.)

heterogeneous data

4


Introduction – Motivation

5

? ?

We can not accurately catch users’ preference by analyzing his and his friend’s check-in actives


Introduction – Challenges

6

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?


Proposed Method – UPOI-Mine

7

Online: RecommenderOnline: Recommender

Offline :UPOI-MineOffline :UPOI-Mine

LBSN DatasetLBSN Dataset

Location Types

Social Links

Check-in Data

Individual Preference (IP)

Social Factor (SF)

POI popularity (PP)

User-to-POI Relevance Estimation

User-to-POI Relevance

Matrix

User RequestPOIs Recommendation List

Recommendation Model

Intelligent DataBase System Lab, NCKU, Taiwan8




Location Types

Social Links

Check-in Data


Social Factor (SF)

POI popularity (PP)



Matrix



Feature Extraction


Social Factor (SF)

9

kikiki DisSimwCheckSimw ,,, )1(Relation

||

1,

,, S

ssk

jkjk

checkin

checkinInterest

F

ki,kk,jji Interest,POIuserSF

1

]Relation[)(

F: friends of user iS: the set of POIsU: the set of user i’s friendsCheck-in k,* = check-ins of user k at POI*

Weighted summation:Weight


Social Factor – Relation

10

Check-in Similarity (CheckSim)based on their check-in log

Relative Distance Similarity (DisSim)based on their geographic distance


Relation – CheckSim

11

POI ID A B C D E

user i 1 0 2 5 0

user j 0 10 0 1 0

user k 1 1 0 0 0

user l 1 1 1 1 1

… … … … … …

0.090801010005201

)00()15()02()100()01(2222222222

i,jCheckSim

i j k …

i 0 1 0 …

j 1 0 1 …

k 0 1 0 ..

… … … … …

Friend Indicator


Relation – DisSim

12

Distance dissimilarity

Maxi=1000

i j k …

i 0 100 10 …

j 100 0 50 …

k 10 50 0 ..

… … … … …

9.01000

1001 i,jDisSim

i j k …

i 0 1 0 …

j 1 0 1 …

k 0 1 0 ..

… … … … …

Friend IndicatorDistance


Social Factor – Example

13

Relation:CheckSim(A, B) = 0.5DisSim(A, B) = 0.03

User B#Check-ins at POIK : 10#Total Check-ins : 100

0077.0]03.0)1.01(5.01.0[100

10 User fromFactor ocial

1.0

BS

w

User A

..... User from CFactorSocial

--------------------------------

kA POI touser offactor social

Interest(B, POIK) = 100

10

POIk

..... User from DFactorSocial

..... User from EFactorSocial

?



14

• Individual Preference(IP)• HPrefi,h

• CPrefi,c

as definedfunction indicator an is )I( where,

Pr)1()POI(Pr,

,)(

s,c

HCount

HCountefHIefC

),POIIP(user

HhHg

jg

jhi,h

Ccjcctgi,c

ji

otherwise0

)(POI if1)POI()(

cctgI

jjcctg

highlightcategory


Individual Preference – HPref & CPref

15

POI A(c1) B(c2) C(c2) D(c3) Total

Highlight h1,h2 h1,h2 h2 h3

Check-in count 5 1 2 2 10

16

1516

215 16

2

Highlight HPrefi,h

H1 0.375

H2 0.5

H3 0.125

h1 h2 h3 h4 h5 Total

User1 A,B A,B,C D

Total 5+1 5+1+2 2 0 0 16

User c1 c2 c3 c4

User1 A B,C D

10

5

10

2110

2

Category CPrefi,c

C1 0.5

C2 0.3

C3 0.2

proportion of check-ins of the label


Individual Preference – Example

16

There is only one category for one POI. There are many highlights for one POI. Category CPref

Seafood 0.5

Hotdog & Sausages

0.1

Fast food 0.1

Steak 0.3

User A’s pref table

Highlight HPref

Coffee 0.5

Sightseeing 0.1

Ice Cream 0.1

Cheese 0.3

POI Category: Hotdog & SausagesHighlight: Coffee(12), Cheese(88)

POI Category: Hotdog & SausagesHighlight: Coffee(12), Cheese(88)

Counts of highlight


Individual Preference – Example (cont.)

17

168.0

)100

881.0()

100

125.0()2.01(

1.02.0),U(

2.0

jPOIserAIP

POI ACategory: Hotdog & SausagesHighlight: Coffee(12), Cheese(88)

POI ACategory: Hotdog & SausagesHighlight: Coffee(12), Cheese(88)

CPref

HPref

Category CPref

Seafood 0.5

Hotdog & Sausages

0.1

Fast food 0.1

Steak 0.3

User A’s pref table

Highlight HPref

Coffee 0.5

Sightseeing 0.1

Ice Cream 0.1

Cheese 0.3


POI Popularity (PP)

18

POI Popularity

Relative Popularity of POI

Normalized based on category

. POIith category w same in the which POIs ofset theis where,

POI

jCS

checkins

checkinsRP

CSk

jj

k


POI Popularity – Example

19

FrankCategory: Hot Dogs

FrankCategory: Hot Dogs

0.04032000,100

032,4FrankRP

Hot Dogs Check-in count

Frank 4,032

KKK 25

…… …

total 100,000





Location Types

Social Links

Check-in Data


Social Factor (SF)

POI popularity (PP)



Matrix



Relevance Estimation

20


Relevance Estimation – Example

21

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

Target

Regression-Tree Model

To estimate the relevance of each pair of user to POI, we use these feature to learn a Regression-Tree Model.


Relevance Estimation – Regression-Tree Model

22

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

Intelligent DataBase System Lab, NCKU, Taiwan23




Location Types

Social Links

Check-in Data


Social Factor (SF)

POI popularity (PP)



Matrix



Recommender


Experimental Evaluation

24

Experimental dataset – Gowalla DatasetNear or within New York City1,964,919 POIs18,159 people5,341,191 Check-ins392,246 Friendship Links


Experimental Evaluation

25

Experimental measurements Normalized Discounted Cumulative Gain (NDCG)

To measure ranking performance of relevance score of top k POIs in recommendation list

Mean Absolute Error (MAE)

To measure error of relevance score of all POIs

n

iii yf

nMAE

1

1

bi

i

iGiDCG

biiGiDCG

iiG

iDCG

b

if,log

][]1[

if ],[]1[

1 if ],[

][][

][@

pIDCG

pDCGpNDCG


Experimental Evaluation (cont.)

26

Ground Truth

BaselineTrust 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 Influence

for Collaborative Point-of-Interest Recommendation. Proceedings of SIGIR, pages 1046-1054, Beijing, China, 2011.

avgx-avg

x-avg

avgx-avg

avgx

if ,2min

-3

if ,2max

3POI ID Check-in Relevance

A 50 1

B 50 1

C 500 5

D 200 3

avg = 200


Comparison of Various Features

27

The Individual Preference is more important than Social Factor for urban POI recommendation.


Comparison of Various Features (cont.)

28


Comparison with Existing Recommenders

29


Comparison with Existing Recommenders (cont.)

30


ConclusionsWe proposed a novel urban POIs recommendation which is

called UPOI-Mine by mining users’ preferences.we propose three kinds of useful features

Social FactorIndividual PreferencePOI Popularity

Through a series of experiments by the real dataset GowallaWe 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.


Question?

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Intelligent DataBase System Lab, NCKU, Taiwan Josh Jia-Ching Ying, Eric Hsueh-Chan Lu, Wen-Ning Kuo and Vincent S. Tseng Institute of Computer Science