trust and recommender systems

1

ZhaYefei

2013.6.24

Trust and Recommender System

2

Outline

Recommender SystemTrust ModelsTrust in Recommender SystemConclusion

3

Recommender System

Information overload Classified catalogue Search

Ask for friends Two-win

Info Producer Info Consumer

Benefit Long tail 2/8

Why ?

4

Recommender System

Application

Amazon

More than 35% sale are from Recommender System!

Rating

Explaination

5

douban FM

hulu Like ?

60% users benefit!

Recommender System

6

Recommender System

Collaborative Filtering

Content-based Filtering

Algorithm

Item-basedUser-based

1st 2nd 3rd

7

Recommender System

Content-based

Filtering

Movie A

Movie B

Movie C

Like

Like

Like

Movie AType :

Love; Romantic

Movie BType :

Horror;Thriller

Movie CType :

Love; Romantic

similar

User A

User B

User C

8

User-basedFiltering

Recommender System

Item A

Item B

Item C

Item D

Like

Recommend

User A

User B

User C

9

Item-based Filtering

Item A

Item B

Item CLike

Recommend

similar

Recommender System

User A

User B

User C

10

Local Trust

PageRank

Models

Mole Trust Tidal Trust

1st 2nd 3rd

Trust

Global Trust

11

Paolo Massa Italy SAC 2005

(Symposium on Applied computing. ACM, 2005)

A Trust-enhanced Recommender System application: Moleskiing

MoleTrust

12

MoleTrust

G

H

I

A

B

C

D

E

F

0 1 2 3

dist

0 A

1 B C D

2 E F

3 G H I

13

MoleTrust

A

B

C

D

E

F

Setp1 --(BFS) dist=0,1,2 user[dist] user[dist-1]

dist=0, user[0]= Adist=1, user[1]=B,C,Ddist=2, user[2]=E,F

Setp2 trust(A)=1 For each dist =1,2,…

14

( )

( )

( ( )* ( , ))

( )( )

i pre u

i pre u

trust i edge i u

trust utrust i

Setp2 For each u in user[dist]

trust(i=pre(u)) >=0.6

eg.

A

B

C

D

E

F

0.8

0.7

0.5

0.8

0.7

0.7

0.8

dist=1 : Trust(B)=0.8; Trust(C)=0.7; Trust(D)=0.5;dist=2: Trust(E)=(0.8*0.6+0.7*0.7)/(0.8+0.7)=0.65 Trust(F)= (0.7*0.7)/0.7=0.7

MoleTrust

15

Jennifer Ann GolbeckUniversity of Maryland Ph.D thesis 2005

Computing and Applying Trust in Web-base Social Networks

TidalTrust

16

TidalTrust

G

H

I

A

B

C

D

E

F

( )

| ( ) |

jsj adj i

is

t

tadj i

1st

: the trust rating from node i to node jijteg.

2AB AC

AE

t tt

2AE AF

AG

t tt

( )is jst f t

17

TidalTrust

G

H

I

A

B

C

D

E

F

: the trust rating from node i to node j

ijt

2nd

( )

( )

ij jsj adj i

isij

j adj i

t t

tt

3rd

( ) max

( ) max

ij

ij

ij jsj adj i t

isij

j adj i t

t t

tt

18

TidalTrust

Sc

9 8 10

9 9

Sk

8 6

8

89

9 91010

9

Choose The Max as Threshold

2nd

Maximum

9 8 10

8910

9

1st Min=8Min=8 Min=9

9

Setp1 --(BFS)

19

TidalTrust

Sc

9 8 10

9 9

Sk

8 6

8

89

9 91010

9

Choose The Max as Threshold

The shortest path Num=3 Setp2

Max( Strength Paths to Sink )

Max(9,9)=9

20

MoleTrust VS. TidalTrust

G

H

I

A

B

C

D

E

F

MoleTrust: Trust(AG) => Trust(AE)Trust(EG)

AB E

GTidalTrust: Trust(AG) => Trust(AB)Trust(BG)

A

B EG

21

PageRank

A

C

B

D

E

1

1

( )( )( ) (1 ) ( ... )

( ) ( )n

n

PR tPR tPR A d d

C t C t

eg.( ) ( )

( ) (1 0.85) 0.85*( )1 3

PR B PR CPR A

1..

1..

( )* ( )( ) (1 ) ( )

( )

i ii n

ii n

C t RP tRP A d d

C t

?

Trust-aware Recommender Systems

Trust in Recommender Systems

Paolo Massa ItalyRecSys2007

John O’Donovan University College Dublin(Ireland) IUI2005

(International Conference on Intelligent User Interfaces)

Trust in Recommender System

23

Trust


Collaborative FilteringData sparsityBe easily attacked

24


( )

( )

( , )( ( ) )

( )| ( , ) |

p P i

p P i

sim c p p i p

c i csim c p

Pure Collaborative Filtering: 1st . User Similarity

2nd. Rating Predictor

P(i): User similarity of c

c(i): Rating predicted for item i by c

p(i): Rating for item i by a producer p

sim(c, p):Similarity between c and p

25

Trust [N*N]

Rating [N*M]

Input

N: UsersM: Items

Trust Metric

EstimatedTrust[N*N]

SimilarityMetric

User [N*N]Similarity

Rating Predictor

PredictedRating[N*M]

OutputFirst step Second step

Pure Collaborative Filtering


26

From the Epinions.com Web site49,290 users who rated a total of139,738 different items at least once,

writing664,824 reviews.487,181 issued trust statements.

Consists of 2 filesRatings dataTrust data

Experimental Analysis

Dataset

Experimental Analysis

Introduce Recommender System、MoleTrust、 TidalTrust、 PageRank

Trust is very effective in alleviating RSs weaknesses: Data sparsity; Be easily attacked; Cold-start.

Trust propogation is a tradeoff in terms of Accuracy and Coverage;

Conclusion

30

Thanks for your attention !

trust and recommender systems

Education