Intelligent DataBase System Lab, NCKU, Taiwan Josh Jia-Ching Ying 1, Eric Hsueh-Chan Lu 2 and...

Intelligent DataBase System Lab, NCKU, Taiwan

Josh Jia-Ching Ying1, Eric Hsueh-Chan Lu2 and Vincent S. Tseng1

1Institute of Computer Science and Information EngineeringNational Cheng Kung University

No.1, University Road, Tainan City 701, Taiwan (R.O.C.)

2 Department of Computer Science and Information EngineeringNational Taitung University

No.684, Sec. 1, Zhonghua Rd., Taitung City, Taitung County 950, Taiwan (R.O.C.)

Followee Recommendation on Asymmetric Location-Based Social Network


Outline

2

Introduction

Symmetric V.S. Asymmetric

Motivation

Challenges

Geographic-Textual-Social Based Followee Recommendation (GTS-FR)

Feature Extraction

Model Building

Experimental Results

Conclusions



Types of Social NetworkSymmetric Social Network

Undirected graphFacebook, Gowalla , Foursquare

Asymmetric Social Networkdirected graphTweeter, Everytrail

Friends V.S. Followees

3

LikeInterest ?A B A B

information

Like?

(a) (b)

LikeInterest ?A B A B

information

Like?

(a) (b)


Introduction – Motivation

Most followee recommendations directly adopt concept of traditional friend recommending system.Friend-of-friend link Geographical distance (or similarity)

Traditional friend recommending system could not work well for followee recommendationSocial feature

followee-of-followeeTweeter, Everytrail, …

Geographic featureSimilarity of users’ trajectoryHGSM [Zheng et al., 2011]

4

Trajectory1Trajectory2Trajectory3

A hiking…

B

Shopping…

C

hiking…



A hiking

B

Shopping…

C

hiking…


Introduction – Motivation

5

Textual Information


Introduction – Challenges

6

How to recommend followees for users based on not only social factors but also user-generated data (textual and geographical data)?

Social properties

Geographical properties

Textual properties

How to build a model for recommending for followee recommendation?


Outline

7

Introduction


Motivation

Challenges


Feature Extraction

Model Building


Conclusions


Feature Extraction

8

Social Property (SP)

Geographical Property (GP)

Textual Property (TP)



9

i

jk

a

b

i

jk

a

b

),

)(),(vT(ut

ttyTransitivivuSP

where T(u, v) indicates the set of Transition-Setters of all followee-of-followee links from u to v.

j and a are Transition-Setters of followee-of-followee links i to b

)()(),( atyTransitivijtyTransitivibiSP


Social Property – Transitivity

10

Transitivity by Links between Followees and Followers (LinkTran)based on users’ following relation

Transitivity by Communications between Followees and Followers (CTran)based on users’ textual information



11

Transitivity by Links between Followees and Followers (LinkTran)

Proportion of the user’s followers who also follow the his followees

i

jk

a

b

i

jk

a

b

)( )(

),()()(

1)(

iPj iSk

kjIiSiP

iLinkTran

The followers of user j are user a user i, and user k.

The followee of user j is user b.

Thus, the LinkTran of user j is

(1+0+0)/(3×1) 0.33 ≒



12

Transitivity by Communications between Followees and Followers (CTran)

otherwise , 0

user follows user if , }),({max

),(1

),()(

kjfjComment

kjComment

kjCTranjSf

)( )(

),()()(

1)(

iPj iSk

kjIiSiP

iLinkTran


Feature extraction

13






14

where Tr(u) indicates the set of trajectories of user u.

)( )(

),()()(

1),(

uTrp vTrq

qpSimilarityvTruTr

vuGP

… …

u vSimilar?


Trajectory Similarity

15

p1 p2 p3p4

p5 p6

p7p8

p9

p10

s1 s2

Location2

Location5

p1 p2 p3p4

p5 p6

p7p8

p9

p10

s1 s2

Location2

Location5

<location2, location5>

Y. Zheng, L. Zhang, and X. Xie. Recommending friends and locations based on individual location history. ACM Transaction on the Web, 2011.


Trajectory Similarity

16

3

2

||

|),(|)),,((

4

2

||

|),(|)),,((

Q

QPLCSQQPLCSratio

P

QPLCSPQPLCSratio

12

7

2

)),,(()),,((),(),(

QQPLCSratioPQPLCSratioQPIQPSimilarity TEA

7

4)),,(()),,((),(),(

QP

QQPLCSratioQPQPLCSratioPQPIQPSimilarity TWA

P = <A, B, C, D> Q = <A, D, C>their longest common sequence is LCS(P, Q) = <A, C>

where IT(P, Q) is an indicator function which indicates whether the tags of P and Q are the same.


Feature extraction

17





Textual Property (GP)

18

w1

w2

w3

w4

w5

User-Keyword (UK) Graph

User ui has texted keyword wj in some textual information for cij times.

HITS-Based random walk modelM = [cij ]

c12

12

1

11

))1((

))1((

kkeywordrow

kuser

kuser

Tcol

kkeyword

xMx

xMx

UK-based Textual Property

Users comment other users’ trip or write travelogue within their trips can represent their information needs.


Textual Property (GP)

19

14

1

13

1

12

1

11

))1((

))1((

))1((

))1((

kkeywordrow

kuser

klocationrow

kkeyword

kkeyword

Tcol

klocation

kuser

Tcol

kkeyword

yMx

yNy

xNy

xMx

w1

w2

w3

w4

w5

l2

l3

l4

l5

l6

l1

l7

Location-Keyword (LK) Graph

Location ls has been texted with keyword wj in comments or travelogues for vsj times.

HITS-Based random walk model N = [vsj ]

vsj

ULK-based Textual Property

similar keywords could be texted with the similar locations.


Outline

20

Introduction


Motivation

Challenges


Feature Extraction

Model Building


Conclusions


Model Building

21

User ID Textual Properties

Geographical Properties

Social Properties

Follow

j 0.7 0.2 0.5 1 (ij)

… … … … …

m 0.1 0.9 0.5 0 (ij)

SVM, Logistic Regression, C4.5, …

We choose SVM as the classifier because it has shown excellent performance in similar tasks

user i


Outline

22

Introduction


Motivation

Challenges


Feature Extraction

Model Building


Conclusions


Experimental Evaluation

23

Experimental dataset – EveryTrail DatasetWe extract the data from 12/2011 to 3/2012, each

month is a time period. We got 35,153 users and 4 snapshots.

Snapshot 1st 2nd 3rd 4th

# of trips 116179 145,662 193,331 196,949

# of comments 337,519 293,453 315,585 379,020

# of links 700,103 777,738 1,056,077 1,139,832


Experimental Evaluation

24

Experimental measurements

p- indicate the number of incorrect recommendations p+ indicate the number of correct recommendations R indicates the total number of links in the testing data

pp

pPrecision

R

p

Recall

RecallPrecision

RecallPrecision2measure-F


Comparison of Various Features

25

The Textual Properties is more effective than other properties for followee recommendation.


Comparison of Various Features (cont.)

26

In detail, The ULK-based Textual Property is more effective than other properties for followee recommendation.


Comparison with Existing Recommenders

27

To compare Existing Recommenders, our approach significantly outperform other Recommenders.


ConclusionsWe have proposed a novel approach named Geographic-

Textual-Social Based Followee Recommendation (GTS-FR) for followee recommendation.

We propose three kinds of useful featuresSocial Property (SF), Geographical Property (GP) Textual Property (TP)

Through a series of experiments by the real dataset obtained from EverTrail, we have validated our proposed GTS-FR and shown that GTS-FR has excellent effectiveness.


Question?

Thank you for your attentions

Intelligent DataBase System Lab, NCKU, Taiwan Josh Jia-Ching Ying 1, Eric Hsueh-Chan Lu 2 and...

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