social recommender system
TRANSCRIPT
Social Recommender System
2015/4/10 1Middleware, CCNT, ZJU
Yueshen XuMiddleware, CCNT, ZJU
Knowledge
Engineering
&
E-Commerce
Outline
2015/4/10 2Middleware, CCNT, ZJU
Where from?
How to recommend?
What to recommend?
What’s the problem?
ML & DM
Related Topics
Trends
What’s your
perspective?
Basic, Generalized, Comprehensible
Introduction
Social Overload
Facebook largest social network site
– 600,000,000 users
YouTube largest video sharing site
– 2,000,000,000
Twitter largest microblogging site
– 65,000,000 tweets per day
Sina microblog largest microblogging site in
China
– 400,000,000 users
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Introduction
The Recommender Systems is an augmentation
of the social process
Any CF system has social characteristics
Social Media and Recommender Systems can
mutually benefit each other
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Introduction
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Real-world examplesWhy?
Different man,
Different news
Pioneer
‘....based on
recommendatio
n algorithms....’
Multi-Media
Fundamental Recommendation Approaches
Collaborative filtering based Recommendation
Aggregate ratings of objects from users and generate
recommendation based on inter-user/inter-item
similarity
Demographic Recommendation
Age,gender,income…
Content-based Recommendation
Music gene
Hybrid Methods
Mixed
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Your imagination
Fundamental Recommendation Approaches
In the real world, we seek advices from our
trusted people
CF automate the process of ‘word-of-mouth’
Select a subset of the users(neighbors) to use as
recommenders
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Collaborative Filtering
Fundamental Recommendation Approaches
Shall we recommend Superman for John?
Jon’s taste is similar to both Chris and Alice tastes
Do not recommend Superman to him
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User based CF algorithm
Fundamental Recommendation Approaches
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User based CF algorithm
vi - the mean vote for user i
k - a normalization factor
pij – the predicitive vote
w(i,j ) – the similarity between ui and uk !
Cose based similarity Pearson Based similarity
Fundamental Recommendation Approaches
The transpose of the user-based algorithms
Bob dislike Snow-white(which is similar to Shrek)
Do not recommend
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Item based CF algorithm
W(k,j) is a measure of item similarity – usually the cosine measure
Matrix Factorization
Matrix Decomposition
Tri-angle
LU
QR
Spectral
SVD
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Matrix Factorization
SVD-like
Non-negative
PMF
BPMF
pLSA, LDA
Matrix
Theory
Machine
Learning
Discriminative Model
Generative Model
Unsupervised
Learning
Matrix Factorization---SVD : the ancestor
Rudiment---Singular Value Decomposition
For an arbitrary matrix A there exists a factorization
named SVD, as follows:
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Matrix Factorization---Latent Semantic Analysis PTM LDA
Low-rank matrix factorizationWhy factorizing?
– One is about the interpretation
– You prefer Lost in Thailand ‘cause it’s a drama, and X, and
Y, and Z, and ......
– X, Y & Z are named as latent factors
So matrix factorization can be come across as
another type of LSA(Latent Semantic Analysis)
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Share us
sth
corssing
your mind
Probabilistic
Topic Model !
Matrix Factorization---SVD-Like : low-rank matrix factorization
Latent Factor Model Generative Model
Low-rank matrix factorization Latent Factor Space
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QPRR T
QPRR T
QPRR T
QPRR T
QPRR T
QPRR T
Rating
Matrix
Approximate
Rating Matrix User Latent
Factor Matrix
Item Latent
Factor Matrix
ff
ifufui fiQfuPqpriuR ),(),(),(
Predicted value ),( jiR
kk
ikuk kiQkuPqpjirjiR ),(),(),(),(
k-rank
factors
Basic Form
Matrix Factorization---SVD-Like : low-rank matrix factorization
Minimize the sum-squared errors
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Skip
Details
m
i
n
j
j
T
iijQP
QPR1 1
2
, 2
1min
m
i
n
j
j
T
iijijQP
QPRI1 1
2
,)(
2
1min
Frobenius Form
Just like Quadratic regression
I : the indicator function
Regularization
Avoid overfitting Why? Sparsity/Sample
Shortage
2221
1 1
2
, 22)(
2
1min
FF
m
i
n
j
j
T
iijijQP
QPQPRI
Solution
Stochastic Gradient Descent
Matrix Factorization---PMF : the production of Bayesian Theory
SVD-Like is not perfect Why?
Subject & Object the victim of formalism
Maximum Posterior Probability(MAP)
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)()(),|()|,( VpUpVURpRVUp
m
i
n
j
I
Rj
T
iij
Rij
VUrNVURp1 1
2,|),|(
m
i
UiU IUNUp1
22 ),0|()|(
n
j
ViV IVNVp1
22 ),0|()|(
Gaussian
Noise
n
j
Vi
m
i
Ui
m
i
n
j
I
Rj
T
iij IVNIUNVUrNRVUpRij
1
2
1
2
1 1
2 ),0|(),0|(,|)|,(
Zero-mean spherical Gaussian prior
Surroundings
Topics related
Non-negative Matrix Factorization
– Deng Cai etc.
Boltzmann Machines
– Discarded
Heterogeneous networks
– Prof. Han
– Link Prediction & Community Discovery
Transfer Learning & Online Learning
– Qiang Yang etc.
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Excavate
Structures
Neural
Network
‘Graph
Regularized
NMF for.....’
Different
Certain
Networks
Online
Algorithms
Others
• Semantic
Web
• Ranking
• Computing
Ads
• Network
Marketing
• Clustering
• NLP
• TM
• Sociology
• Etc.
Trends---Horizontal Expansion
More Relationship More Matrix
Social Network
– Turn to your friends for suggestion
Trust Network
– Turn to who you trust for suggestion
Clarify the connection
– What’s the relationship?
– Why does it work?
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Weight &
Relationship
Social/Trust
Network Etc.
Structure of Networks
Trends---Vertical Expansion
3-4-5- Dimensions Tensor
A tensor can be represented as a multi-dimensional
array of numerical values.
– 1-dimensional tensor : Vector
– 2-dimensional tensor : Matrix
Tensor Decomposition & Tensor Factorization
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observed
value
3th, Latent factor,
Time or Tag
1th Latent factor
one, User
2thLatent factor ,
Item
2015/4/10 20Middleware, CCNT, ZJU
Social Recommender System