Download - Trust and Recommender Systems
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ZhaYefei
2013.6.24
Trust and Recommender System
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Outline
Recommender SystemTrust ModelsTrust in Recommender SystemConclusion
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Recommender System
Information overload Classified catalogue Search
Ask for friends Two-win
Info Producer Info Consumer
Benefit Long tail 2/8
Why ?
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Recommender System
Application
Amazon
More than 35% sale are from Recommender System!
Rating
Explaination
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douban FM
hulu Like ?
60% users benefit!
Recommender System
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Recommender System
Collaborative Filtering
Content-based Filtering
Algorithm
Item-basedUser-based
1st 2nd 3rd
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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
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User-basedFiltering
Recommender System
Item A
Item B
Item C
Item D
Like
Recommend
User A
User B
User C
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Item-based Filtering
Item A
Item B
Item CLike
Recommend
similar
Recommender System
User A
User B
User C
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Local Trust
PageRank
Models
Mole Trust Tidal Trust
1st 2nd 3rd
Trust
Global Trust
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Paolo Massa Italy SAC 2005
(Symposium on Applied computing. ACM, 2005)
A Trust-enhanced Recommender System application: Moleskiing
MoleTrust
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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
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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,…
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( )
( )
( ( )* ( , ))
( )( )
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
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Jennifer Ann GolbeckUniversity of Maryland Ph.D thesis 2005
Computing and Applying Trust in Web-base Social Networks
TidalTrust
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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
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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
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TidalTrust
Sc
9 8 10
9 9
Sk
8 6
8
89
9 91010
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Choose The Max as Threshold
2nd
Maximum
9 8 10
8910
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1st Min=8Min=8 Min=9
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Setp1 --(BFS)
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TidalTrust
Sc
9 8 10
9 9
Sk
8 6
8
89
9 91010
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Choose The Max as Threshold
The shortest path Num=3 Setp2
Max( Strength Paths to Sink )
Max(9,9)=9
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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
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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
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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
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Trust
Trust in Recommender System
Collaborative FilteringData sparsityBe easily attacked
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Trust in Recommender System
( )
( )
( , )( ( ) )
( )| ( , ) |
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
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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
Trust in Recommender System
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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
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
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Thanks for your attention !