social recommender system

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  • Social Recommender System

    2015/4/10 1Middleware, CCNT, ZJU

    Yueshen XuMiddleware, CCNT, ZJU

    xyshzjucs@zju.edu.cn

    xyshzjucs@gmail.com

    Knowledge

    Engineering

    &

    E-Commerce

  • Outline

    2015/4/10 2Middleware, CCNT, ZJU

    Where from?

    How to recommend?

    What to recommend?

    Whats the problem?

    ML & DM

    Related Topics

    Trends

    Whats your perspective?

    Basic, Generalized, Comprehensible

  • Introduction

    Social OverloadFacebook 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

    2015/4/10 Middleware, CCNT, ZJU 3

  • 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

    2015/4/10 Middleware, CCNT, ZJU 4

  • Introduction

    2015/4/10 Middleware, CCNT, ZJU 5

    Real-world examplesWhy?

    Different man,

    Different news

    Pioneer

    ....based on recommendatio

    n algorithms....

    Multi-Media

  • Fundamental Recommendation Approaches

    Collaborative filtering based RecommendationAggregate ratings of objects from users and generate

    recommendation based on inter-user/inter-item

    similarity

    Demographic RecommendationAge,gender,income

    Content-based RecommendationMusic gene

    Hybrid MethodsMixed

    2015/4/10 Middleware, CCNT, ZJU 6

    Your imagination

  • Fundamental Recommendation Approaches

    In the real world, we seek advices from our trusted people

    CF automate the process of word-of-mouthSelect a subset of the users(neighbors) to use as

    recommenders

    2015/4/10 Middleware, CCNT, ZJU 7

    Collaborative Filtering

  • Fundamental Recommendation Approaches

    Shall we recommend Superman for John?Jons taste is similar to both Chris and Alice tastes

    Do not recommend Superman to him

    2015/4/10 Middleware, CCNT, ZJU 8

    User based CF algorithm

  • Fundamental Recommendation Approaches

    2015/4/10 Middleware, CCNT, ZJU 9

    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 algorithmsBob dislike Snow-white(which is similar to Shrek)

    Do not recommend

    2015/4/10 Middleware, CCNT, ZJU 10

    Item based CF algorithm

    W(k,j) is a measure of item similarity usually the cosine measure

  • Matrix Factorization

    Matrix DecompositionTri-angle

    LU

    QR

    Spectral

    SVD

    2015/4/10 Middleware, CCNT, ZJU 11

    Matrix FactorizationSVD-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 DecompositionFor an arbitrary matrix A there exists a factorization

    named SVD, as follows:

    2015/4/10 Middleware, CCNT, ZJU 12

  • Matrix Factorization---Latent Semantic Analysis PTM LDA

    Low-rank matrix factorizationWhy factorizing?

    One is about the interpretation

    You prefer Lost in Thailand cause its 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)

    2015/4/10 Middleware, CCNT, ZJU 13

    Share us

    sth

    corssing

    your mind

    Probabilistic

    Topic Model !

  • Matrix Factorization---SVD-Like : low-rank matrix factorization

    Latent Factor Model Generative ModelLow-rank matrix factorization Latent Factor Space

    2015/4/10 Middleware, CCNT, ZJU 14

    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

    2015/4/10 Middleware, CCNT, ZJU 15

    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

    RegularizationAvoid 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)

    2015/4/10 Middleware, CCNT, ZJU 16

    )()(),|()|,( 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 relatedNon-negative Matrix Factorization

    Deng Cai etc.

    Boltzmann Machines Discarded

    Heterogeneous networks Prof. Han

    Link Prediction & Community Discovery

    Transfer Learning & Online Learning Qiang Yang etc.

    2015/4/10 Middleware, CCNT, ZJU 17

    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

    Whats the relationship?

    Why does it work?

    2015/4/10 Middleware, CCNT, ZJU 18

    Weight &

    Relationship

    Social/Trust

    Network Etc.

    Structure of Networks

  • Trends---Vertical Expansion

    3-4-5- Dimensions TensorA tensor can be represented as a multi-dimensional

    array of numerical values.

    1-dimensional tensor : Vector

    2-dimensional tensor : Matrix

    Tensor Decomposition & Tensor Factorization

    2015/4/10 Middleware, CCNT, ZJU 19

    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