A brief introduction of the knowledge of Trust and Recommender systems.

1. ZhaYefei
2013.6.24
1
Trust and Recommender System

2. Outline
 Recommender System
 Trust Models
 Trust in Recommender System
 Conclusion

3. Recommender System
 Information overload
 Classified catalogue
 Search
 Ask for friends
 Two-win
 Info Producer
 Info Consumer
 Benefit Long tail
Why ?

4. Recommender System
Application
Amazon
More than 35% sale are from Recommender
System!
Rating
Explainatio
n

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 A
Type :
Love; Romantic
Movie B
Type :
Horror;Thriller
Movie C
Type :
Love; Romantic
similar
User A
User B
User C

8. User-based
Filtering
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 C
Like
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]= A
dist=1, user[1]=B,C,D
dist=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 u
trust 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 Golbeck
University 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
( )
| ( ) |
js
j adj i
is
t
t
adj i
∈
=
∑
1st
: the trust rating from node i to node jijt
eg.
2
AB AC
AE
t t
t
+
=
2
AE AF
AG
t t
t
+
=
( )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 js
j adj i
is
ij
j adj i
t t
t
t
∈
∈
=
∑
∑
3rd
( ) max
( ) max
ij
ij
ij js
j adj i t
is
ij
j adj i t
t t
t
t
∈ ∩ ≥
∈ ∩ ≥
=
∑
∑

18. TidalTrust
S
c
9 8 1
0
9 9
S
k
8 6
8
8
9
9 9
10
10
9
Choose The Max as Threshold
2nd
Maxim
9 8 1
0
8
9
10
9
1s
t
Min=8
Min=8 Min=9
9
 Setp1 --(BFS)

19. TidalTrust
S
c
9 8 1
0
9 9
S
k
8 6
8
8
9
9 9
10
10
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)
A
B E
G
TidalTrust: Trust(AG) => Trust(AB)Trust(BG)
A
B E
G

21. PageRank
A
C
B
D
E
1
1
( )( )
( ) (1 ) ( ... )
( ) ( )
n
n
PR tPR t
PR A d d
C t C t
= − + + +
eg. ( ) ( )
( ) (1 0.85) 0.85*( )
1 3
PR B PR C
PR A = − + +
1..
1..
( )* ( )
( ) (1 ) ( )
( )
i i
i n
i
i n
C t RP t
RP A d d
C t
=
=
= − +
∑
∑
?

22. Trust-aware Recommender Systems
 Trust in Recommender Systems
 Paolo Massa
 Italy
RecSys2007
John O’Donovan
 University College Dublin(Ireland)
 IUI2005
(International Conference on Intelligent User Interfaces)
Trust in Recommender System

23. Trust
Trust in Recommender System
Collaborative Filtering
Data sparsity
Be easily attacked

24. Trust in Recommender System
( )
( )
( , )( ( ) )
( )
| ( , ) |
p P i
p P i
sim c p p i p
c i c
sim 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: Users
M: Items
Trust
Metric
Estimated
Trust[N*N
]
Similarity
Metric
User
[N*N]
Similarity
Rating
Predictor
Predicted
Rating
[N*M]
Output
First step Second step
Pure Collaborative Filtering
Trust in Recommender System

26. From the Epinions.com Web site
49,290 users who rated a total of
139,738 different items at least
once, writing
664,824 reviews.
487,181 issued trust statements.
Consists of 2 files
Ratings data
Trust data
Experimental Analysis
Dataset

27. Experimental Analysis

28. Experimental Analysis

29.  Introduce Recommender System 、 MoleTrust 、 TidalTrust 、 PageRa
nk
 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 !