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Social Web Search Filippo Menczer Leveraging online social behavior for collaborative Web search Informatics and Computer Science

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  • Social Web SearchFilippo Menczer

    Leveraging online social behavior for collaborative Web search

    Informatics and Computer Science

  • NaN:Networks & agents Network

    http://homer.informatics.indiana.edu/~nan/

  • Outline

    • Part 1: GiveALink.org

    • Part 2: 6S

  • links

    text

    author social

    Cue

    s

    Markup

  • news research

    + = σ★ ☆★★☆★ ☆☆

    σA★ ☆☆★

    σB

    fun

  • FA(x) FA(y)

    + = σ(x,y)

    FB(a(x,y))

    σ(x, y) =1

    N

    N∑

    u=1

    2 log

    (|Fu[a(x,y)]|

    |Ru|

    )

    log|Fu(x)|

    |Ru| + log|Fu(y)||Ru|

  • 10-16

    10-14

    10-12

    10-10

    10-8

    10-6

    10-4

    10-1

    100

    101

    102

    103

    104

    Fra

    ctio

    n o

    f U

    RL

    s

    Degree

    smin = 0

    smin = 0.0025

    smin = 0.0055

    Pr(k) !! exp(-0.002*k)

    Pr(k) !! k-2

    degree

    UR

    Ls

  • GoogleGaL

    RelevantSet

    UserStudy

  • c(x) =1

    |U |∑

    y∈U

    1 + minx!y∑

    (u,v)∈x!y

    (1

    σ(u, v)−1

    )

    −1

    pt+1(x) = (1 − α) + α ·∑

    y∈U

    σ(x, y) · pt(y)∑z∈U σ(y, z)

    Centrality&

    Prestige

  • Ranking &

    Recommendation by Novelty

    ν(x, y) =

    [1 + min

    z∈U

    (1

    σ(x, z)+

    1

    σ(z, y)− 2

    )]−1

    σ(x, y)

    x

    y

    z

  • Personalization

    η(x, A) = maxy∈A

    [σ(x, y) · log

    (N

    N(y)

    )]

  • spam

    collaborative filtering, social semantic similarity, unlinked pages, multimedia content, trust

    scalability,

    density

  • Questions?

  • Outline

    • Part 1: GiveALink.org

    • Part 2: 6S (next semester)