1 Intelligent E-Commerce Systems Laboratory,

INHA UNIVERSITYINHA UNIVERSITYINCHEON, KOREAINCHEON, KOREA

http://eslab.inha.ac.kr/

Collaborative Tagging in Collaborative Tagging in Recommender SystemsRecommender Systems

AE-TTIE JIAE-TTIE JI11, CHEOL YEON, CHEOL YEON11, HEUNG-NAM KIM, HEUNG-NAM KIM11, AND GEUN-SIK JO, AND GEUN-SIK JO22

11 Intelligent E-Commerce Systems Laboratory, Intelligent E-Commerce Systems Laboratory, Department of Computer Science & Information Engineering, Inha University Department of Computer Science & Information Engineering, Inha University

{{aerry13, , entireboy, , nami}@eslab.inha.ac.kr}@eslab.inha.ac.kr

22 School of Computer Science & Engineering, Inha University, School of Computer Science & Engineering, Inha University, 253 Yonghyun-dong, Incheon, Korea 402-751 253 Yonghyun-dong, Incheon, Korea 402-751

[email protected]

- 2 -INHA UNIVERSITYINHA UNIVERSITYINCHEON, KOREAINCHEON, KOREA


IntroductionIntroduction

Recommender System with Recommender System with Collaborative TaggingCollaborative Tagging

Experimental ResultsExperimental Results

Conclusions Conclusions

Future WorksFuture Works



















ConclusionsConclusions










?? ?



Collaborative Filtering (CF)Collaborative Filtering (CF)


!! !

Nearest neighbors’ opinion

My preference history

Recommendation

Sparsity Problem

Cold-startUser

Problem



Collaborative Tagging (CT)Collaborative Tagging (CT)

cookin

g, hobby

myC

yworld

picn

ic, 2

007,

hor

se

picn

ic, h

orse

walkin

g, p

icnic

coup

le, w

alki

ng, p

icni

c

wiki, t

aggi

ng

tagg

ing,

wik

iped

ia




MotivationMotivation

!! !

Nearest neighbors’ tags

My tags

walking, picnic

picnic, 2007, horse, girl

Recommendation

picnic, horse

cooking, hobby, spaghetti

tagging, wikipedia, social

intelligence




System ArchitectureSystem Architecture

Recommender System with CTRecommender System with CT

Part 1: Catching an user’s latent preference!Part 1: Catching an user’s latent preference! Candidate Tag Set Generation via CF

Part 2: Probabilistic Recommendation!Part 2: Probabilistic Recommendation! Naïve Bayesian Approach

User CandidateTag Model

User-Tag MatrixA : r ⅹ m

User-User Similarity[r ⅹ r]

Naïve Bayesian Classifier

User-Item MatrixR : r ⅹ n

Tag-Item MatrixQ : m ⅹ n

Recommendation

T3

T5T1T2T4

T

Users

Items

Tagging

Tags

......

..........

k:: users, n:: items, m:: tagsr:: users, n:: items, m:: tags

Target User



Matrices Representing Matrices Representing PreferencesPreferences

User-item binary matrix, User-item binary matrix, RR ( (rr × × nn)) Ru,i : whether a user ur prefers an item in or not.

User-tag matrix, User-tag matrix, AA ( (rr × × mm)) Au,t : frequency of a tag tm tagged by a user ur.

Tag-item matrix, Tag-item matrix, QQ ( (mm × × nn)) Qt,i : frequency of a tag tm for an item in.

1

i1

u1

i2 i3 in

(a) user-item binary matrix R

itemuser

u2

u3 1

u4 1

...… …

ur 1

1

1

…

1

i4

1

...

…

itemtag

…

…

…

…

21

1

3

1

1

taguser

3 1

5

2 4

(b) user-tag matrix A

3

2

t1

u1

t2 t3

u2

u3

u4

t4 i1

t1

i2 i3

t2

t3

t4

i4

…

1

...… … …

...

…

…

…

…

… …...… … …

...

…

…

…

…

… …

3 3ur

1

2

tm

(c) tag-item matrix Q

1 2tm

2

in




Recommendation ProcessRecommendation Process


Step 1: CTS Generation

t1

u1

u2

u3

u4

t2 t3 t4 i1

t1

t2

t3

t4

i2 i3 i4 taguser

itemtag

Naïve Bayes Classifier

Target user

CTS for user u3

Step 2: Recommendation

Collaborative Filtering

TopN items for user u3

Candidate Tag Set (Candidate Tag Set (CTSCTS) Generation via CF) Generation via CF User-User Similarity Tag Preference



CTS Generation via CFCTS Generation via CF

CTSCTS (Candidate Tag Set) (Candidate Tag Set) The latent preference of a target user

User-user SimilarityUser-user Similarity To find k nearest neighbors (KNN) of a target user based on

user-tag matrix A

Tag PreferenceTag Preference

Tt v,tTt u,t

Tt v,tu,t

)(A)(A

AA

)v,u(sim(u,v)

22

cos

)( ,, ),()(uKNNo totu ousimAS

t1

u1

u3

u2

u4

t2 t3 t4

2

4 1

taguser

Target user

u5

t5

2

1

2 3

3

4 1

3 2

user-tag matrix A


T} tw,,,,| x {t(u) CTS xxw 2 1



Case for Data SparsityCase for Data Sparsity

iGoo

gle

(ww

w.g

oogl

e.co

m/ig

)

Brandon

Courtney

Dannis

Net

vibe

s(w

ww

.net

vibe

s.co

m)

MS

Virt

ual E

art

h(m

aps.

live.

com

)

Win

dow

s Li

ve(w

ww

.live

.com

)

1

1

itemuser

1

11

1 1

targetuser

Users Tags

Courtney

Brandon

Dannis

Contents

MS Virtual Earth(maps.live.com)

iGoogle(www.google.com/ig)

Netvibes(www.netvibes.com)

Windows Live(www.live.com)

Improving limitations of CF via CTImproving limitations of CF via CT



iGoo

gle

(ww

w.g

oo

gle

.co

m/ig

)

Brandon

Courtney

Dannis

Net

vibe

s(w

ww

.ne

tvib

es.

com

)

MS

Virt

ual E

arth

(ma

ps.

live

.co

m)

Win

dow

s Li

ve(w

ww

.live

.co

m)

1

1

itemuser

1

11

1 1

pers

onal

Brandon

Courtney

Dannis

web

2.0

map

port

al taguser iG

oogl

e

sear

ch

MS

112 2

3 1 1111

21 1 1 11

…

…

……



Windows Live, search, personal, MS

MS Virtual Earth, MS, map, web2.0

Live Search Maps, map, web2.0, MS

Netvibes, web2.0, personal

search, Google, personal, iGoogletargetuser

Users Tags

Netvibes, personal

Courtney

Brandon

Dannis

Contents





web2.0, iGoogle, search








Users Tags

Netvibes, personal

Courtney

Brandon

Dannis

Contents






iGoo

gle

(ww

w.g

oo

gle

.co

m/ig

)

Brandon

Courtney

Dannis

Net

vibe

s(w

ww

.ne

tvib

es.

com

)

MS

Virt

ual E

arth

(ma

ps.

live

.co

m)

Win

dow

s Li

ve(w

ww

.live

.co

m)

1

1

itemuser

1

11

1 1

pers

onal

Brandon

Courtney

Dannis

web

2.0

map

port

al taguser iG

oogl

e

sear

ch

MS

112 2

3 1 1111

21 1 1 11

…

…

……










Users Tags

Netvibes, personal

Courtney

Brandon

Dannis

Contents





cold-startuser

Eric


web2.0, map, MS

iGoo

gle

(ww

w.g

oo

gle

.co

m/ig

)

Brandon

Courtney

Dannis

Net

vibe

s(w

ww

.ne

tvib

es.

com

)

MS

Virt

ual E

arth

(ma

ps.

live

.co

m)

Win

dow

s Li

ve(w

ww

.live

.co

m)

1

1

itemuser

1

11

1 1

Eric 1

Case for Cold-start UserCase for Cold-start User




Recommendation ProcessRecommendation Process


Step 1: CTS Generation

t1

u1

u2

u3

u4

t2 t3 t4 i1

t1

t2

t3

t4

i2 i3 i4 taguser

itemtag

Naïve Bayes Classifier

Target user

CTS for user u3

Step 2: Recommendation

Collaborative Filtering

TopN items for user u3

Item RecommendationItem Recommendation Naïve Bayes Classifier Top-N Items Recommendation



Item RecommendationItem Recommendation

Naïve Bayes ClassifierNaïve Bayes Classifier Posterior probability : a preference probability of user u for an item iy

with CTSw(u)

Prior probability

Item-conditional Tag Distribution

Top-NTop-N Recommendation Recommendation TopNu items with the highest Pu,y , |TopNu| ≤ N and TopNu ∩ Iu = Ø

t1 t2 t3 tw…

iy

Candidate Tag Set for user u, CTSw(u)

Class item

w

jyjyyu i|ItPiIPP

1, )()(

n

g

r

u gu

r

u yuy

R

RiIP

1 1 ,

1 ,)(

m

t yt

yjyj

Qm

QiItP

1 ,

,1)|(




Dataset & Evaluation MetricDataset & Evaluation Metric

DatasetDataset http://del.icio.us (a social bookmarking service)

Training data : 21,653 / Testing data : 5,413 Sparsity level of user-item matrix : 0.9989

Evaluation metricEvaluation metric

usersusers ItemsItems tagstags book markingsbook markings taggingstaggings

1,5441,544 17,39017,390 10,07710,077 27,06627,066 44,68144,681

)(

u

uu

Test

TopNTesturatiohit

100

)(1

k

uratiohitrecall

k

u




Benchmark AlgorithmsBenchmark Algorithms User-based Collaborative Filtering User-based Collaborative Filtering (Badrul Sarwar, and et al., 2000)

Item-based Collaborative Filtering Item-based Collaborative Filtering (Mukund Deshpande, and et al., 2004)

KNNKNN size was set to 50 where the performance increase size was set to 50 where the performance increase rates were diminished for main comparison.rates were diminished for main comparison.


5.0%

5.5%

6.0%

6.5%

7.0%

7.5%

8.0%

8.5%

9.0%

9.5%

10 30 50 70 100

Neighborhood Size (k )

reca

ll

User-based CF

Item-based CF

Recommendation size N = 10



Experiments with CTS sizeExperiments with CTS size

The size of The size of CTSCTS, , ww, can be a significant factor affecting the , can be a significant factor affecting the quality of recommendation.quality of recommendation.

w w was set to 70, which obtained the best quality for main was set to 70, which obtained the best quality for main comparisons.comparisons.


6.0%

6.5%

7.0%

7.5%

8.0%

8.5%

9.0%

10 30 50 70 100

Candidate Tag Set Size (w)

reca

ll

Tag-based CF

Neighbor size k = 50Recommendation size N = 10



Comparisons of Overall PerformanceComparisons of Overall Performance

Sparsity of the collected dataset affected the performances Sparsity of the collected dataset affected the performances of all three methods.of all three methods.

Even though the number of recommended items were Even though the number of recommended items were small, our method outperformed the other two methods.small, our method outperformed the other two methods.


6.0%

7.0%

8.0%

9.0%

10.0%

11.0%

12.0%

10 20 30 40 50

A number of Recommended Items (N )

reca

ll

Tag-based CFItem-based CFUser-based CF

Neighbor size k = 50CTS size w = 70



For cold-start users who do not have enough preference For cold-start users who do not have enough preference information, our method outperformed the other two information, our method outperformed the other two methods.methods.

Comparisons for Cold-start UserComparisons for Cold-start User

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

9.0%

< 3 < 6 < 300Boomarks

reca

llTag-based CF Item-based CF User-based CF


Neighbor size k = 50CTS size w = 70Recommendation size N = 10



ConclusionConclusion

We analyzed the potential of collaborative tagging We analyzed the potential of collaborative tagging system for applying to recommendation.system for applying to recommendation. User-created tags imply users’ preferences about items as well

as metadata about them. Using tags can partially improve data sparsity and cold-start

user problem which are serious limitations of CF recommendation.

Also proposed is a novel recommender system based Also proposed is a novel recommender system based on collaborative tags of users using CF scheme.on collaborative tags of users using CF scheme. Our algorithm obtained better recommendation quality compared

to traditional CF schemes. It provided more suitable items for user preferences even though

the number of recommended items were small.



Future WorkFuture Work

““Noise” tags can be included in CTS.Noise” tags can be included in CTS. Some tags are too personalized or content-criticizable (e.g., bad,

myWork, to read etc.)

They should be treated for more personalized and valuable analysis.

There are common issues of keyword-based analysis.There are common issues of keyword-based analysis. Polysemy, synonymy and basic level variation.

Semantic tagging is an interesting approach to address these issues.

Documents

1 Intelligent E-Commerce Systems Laboratory,