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HITs Implementation

HITs Implementation. Presented by the Amazingly Brilliant John Yankowski and the slightly less brilliant Larry Phillips. Eigen Values and Vectors. Av = λ v ( λ is the Eigenvalue) Each λ corresponds to one Eigenvector v.

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HITs Implementation

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  1. HITs Implementation Presented by the Amazingly Brilliant John Yankowski and the slightly less brilliant Larry Phillips

  2. Eigen Values and Vectors • Av = λv (λ is the Eigenvalue) • Each λ corresponds to one Eigenvector v I don’t know what this means, but Google seems to think its related to Eigen somehow.

  3. The POWER Method!!!! • x(k+1) = Ax(k) • xk -> Dominant Eigenvector • Hey John, What about other methods??

  4. Computing the ultimate authority and hub scores x and y

  5. Steps • Step 1 Initialize y(0) = e; e is a column vector of all ones • Step 2 take x(k) = Lt y(k-1) , y(k) = Lx(k) and simplify to get…

  6. x(k) = Lt L x(k-1)y(k) = L Lt y(k-1) • Computes the dominant eigenvector for the matrices LT L (Authority matrix) and L LT (Hub Matrix)

  7. Benefits of using the dominant eigenvectors of LTL and LLT • Incurs a small cost in comparison with using scores from all documents on Web • Only one document eigenvector needs to be computed: (LTL or LLT)

  8. Authoritative and Hub Matrices • Authoritative means the links are to the website • Hub means the the links shoot out from the website

  9. Mexican Hats? • Yes, Mexican hats. • We submit a query that results in pages 1 and 6, where 1 happens to point to 6

  10. But Hey, What about Sombreros?? • Related nodes can be added to a limited extent to make the search more comprehensive

  11. I need Mexican Hats! • The query results in Matrix L

  12. MSPaint Matrices are Awesome! • From L, we can find the Authoritative and Hub Matrices.

  13. HITs successfully refines the score by computing • Xi(k) = Σ yj(k-1) • Can be written as X(k) = LTy(k-1) which is the power method that will give you the dominate eigenvector

  14. We have vectors, weee!!! • xT = (0 0 .3660 .1340 .5 0) • yT = (.3660 0 .2113 0 .2113 .2113) • Why John, Don’t those add up to 1? • Why yes they do, and thank you for asking. • These numbers give you the ranking for all your Mexican hat web pages. • Auth. Ranking = (6 3 5 1 2 10) • Hub Ranking = (1 3 6 10 2 5) Dangerously close to a Mexican hat, so we’ll count it

  15. Bibliometricity • Yeah, it’s a big word, and we know it • Refers to two documents that are in-laws (related through association).

  16. How does Bibliometricity apply to mexican hats? • LTL = Din + Ccit • LLT = Dout + Cref Mexican Hat in action

  17. How does this apply to the real world? • http://www.teoma.com is a search engine that uses hits technology.

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