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Circle-based Recommendation in Online Social Networks
Xiwang Yang, Harald Steck*, and Yong Liu
Polytechnic Institute of NYU* Bell Labs/Netflix
11
Outline Background & Motivation
Circle-based RS Trust Circle Inference Trust Value Assignment Model Training
EvaluationConclusion & Future work
22
Social Recommenders Everywhere
3
Collaborative Filtering(CF)
Most Used and well-known Approach for Recommendation Finds Users with Similar Interests to the target user
Matrix Factorization(BaseMF)’
Latent features for users Latent features for items Prediction Model
4
P and Q have normal priors
0u dQ
0i dP
,ˆ Tu i m u iR r Q P
Related Work-Social Recommender
Social Recommendation (SoRec) Model CIKM’08 Factorizing social trust matrix together with user rating
matrix
Social Trust Ensemble (STE) Model SIGIR’09 User’s rating influenced by social friends
SocialMF Model RecSys’10 User’s latent feature(taste) influenced by social friends Handle trust propagation in social network
Using whole trust network for item rating prediction
5
Proposed Improvements for Current Social Recommender
Social networks include multiple circles A more refined social trust information—richer
information Want to incorporate circle information into Social
Recommender Ideally, use trust circles specific to an item category
when predict rating in this category• E.g. Trust Circle of “Music”, Trust Circle of “Cars”, etc
6
Proposed Improvements for Current Social Recommender
7
Existing circles(google+, facebook) not corresponding to an item category
Proposed Improvements for Current Social Recommender
In existing multi-category rating datasets, no circle information
User trusts different subsets of friends in different domains(Cars, Music…)
User trusts different friends differently, related to friend’s expertise value
Should use trust circle specific to item category8
Outline Background & Motivation
Circle-based RS Trust Circle Inference Trust Value Assignment Model Training
EvaluationConclusion & Future work
99
Trust Circle Inference User v is in inferred circle c of u iff u trust v in original social
network and both of them have rating in category c
1010
Original Social Network
Inferred circle for category C1
Inferred circle for category C2
Inferred circle for category C3
Trust Value Assignment
CircleCon1: Equal Trust each user in the inferred circle gets assigned
the same trust value
11
( )
( )* ( ),
( )*,
1cu
c cu v u
cu v
v C
S const if v C
S
( )* ( ) ( ), 1 | |,c c cu v u uS C v C
Trust Value Assignment CircleCon2: Expertise-based Trust
assign a higher trust value or weight to the friends that are experts in the circle / category.
Variant a:• Expertise value of user u proportional to u’s number of
ratings in a circle
Variant b:• Expertise based on u’s number of rating in circle and
voting value from u’s followers in this circle
12
CircleCon3: Trust Splitting
Most trust due to followee’s rating in one category Likelihood u2 trusts u1 in C1, C2 ? Infer likelihood proportional on u2’s number of
ratings in C1 and C2. Assign trust value in a category proportional to the
likelihood u2 trusts u1 in a category
13
Original trust link trust link in c1 trust link in c2
CircleCon3: Trust Splitting
Normalize across followees
14
1 2
1 19, 1c c
u uN N 1 2
2 1 2 1
( ) ( ), ,0.9, 0.1c c
u u u uS S
( )
( )* ( ) ( ), , ,
cu
c c cu v u v u v
v C
S S S
Outline Background & Motivation
Circle-based RS Trust Circle Inference Trust Value Assignment Model Training
EvaluationConclusion & Future work
1515
Model Training Training with ratings from each category
Predict user’s rating in category c Input rating: rating in category c Input social network: Circle c
16
( )0ci
( ) ( ) ( ) ( ) ( )*
( ) ( ) 2, ,
( , ) .
( ) ( )* ( ) ( ) ( )* ( ), ,
( ) 2 ( ) 2
( , , , )
1 ˆ( )2
( )( )2
(|| || || || )2
c c c c c
c cu i u i
u i obs
c c c c c c Tu u v v u u v v
all u v v
c cF F
L R Q P S
R R
Q S Q Q S Q
P Q
( ) ( ) ( ) ( ),
ˆ c c c c Tu i m u iR r Q P
( )0 0( ) ( ),ci d u dc cP Q
is the number of items in category c
Solved by gradient descent
is social information weight
Model TrainingTraining with ratings for all categories
Predict user’s rating in category c Input rating: rating from all categories Input social network: Circle c
17
0 0( ) ( ),i d u dc cP Q
( ) ( ) ( ) ( )*
2, ,
( , ) .
( ) ( )* ( ) ( ) ( )* ( ), ,
( ) 2 ( ) 2
( , , , )
1 ˆ( )2
( )( )2
(|| || || || )2
c c c c
u i u iu i obs
c c c c c c Tu u v v u u v v
all u v v
c cF F
L R Q P S
R R
Q S Q Q S Q
P Q
Outline Background & Motivation
Circle-based RS Trust Circle Inference Trust Value Assignment Model Training
EvaluationConclusion & Future work
1818
Epinions Data
19
Performance Metrics
20
2, ,( , )
ˆ( )
| |test
u i u iu i R
test
R RRMSE
R
, ,( , )ˆ| |
| |test
u i u iu i R
test
R RMAE
R
Training with per-category ratings
21
Training with per-category ratings
22
( ) ( ) ( ) ( ) ( )*
( ) ( ) 2 ( ) 2 ( ) 2, ,
( , ) .
( ) ( )* ( ) ( ) ( )* ( ), ,
( , , , )
1 ˆ( ) (|| || || || )2 2
( )( )2
c c c c c
c c c cu i u i F F
u i obs
c c c c c c Tu u v v u u v v
all u v v
L R Q P S
R R P Q
Q S Q Q S Q
Training with ratings from all categories
23
CircleCon3 of training with per-category rating
Conclusions Propose a novel Circle-based Social
Recommendation framework Split original social network to different circles, one circle
corresponding to one item category User trusts different subsets of friends in different
domains(Cars, Music…) User trusts different friends differently, based on friend’s
expertise
Outperforms the state-of-the-art social collaborative filtering algorithms
Show the promising future of circle-construction techniques in Social Recommender
24
Thanks!
Q & A
25
Trust Value Assignment
CircleCon1: Equal Trust
26
CircleCon2: Expertise-based Trust
Variant a: Expertise based on number of rating in a circle
27
CircleCon2: Expertise-based Trust Variant b:
Expertise value based on user’s number of rating in circle and voting value from followers in this circle
28
Dw records the proportions of ratings user w assigned in all categories. It reflects the interest distribution of w cross all categories
( ) ( ) ( )c c cv v vE N
Training with ratings from all categories
29
Training with ratings from all categories
30