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