lecture17-linkanalysis-handout-6-per

Pii0 is ok i pij j 4 introducon to informaon retrieval

Info iconThis preview shows page 1. Sign up to view the full content.

View Full Document Right Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: Random walk can get stuck in dead ­ends.   Makes no sense to talk about long ­term visit rates.   With remaining probability (90%), go out on a random link.   10%  ­ a parameter. ?? Introduc)on to Informa)on Retrieval Sec. 21.2 Introduc)on to Informa)on Retrieval S ec. 21.2.1 Result of telepor*ng Markov chains   Now cannot get stuck locally.   There is a long ­term rate at which any page is visited (not obvious, will show this).   How do we compute this visit rate?   A Markov chain consists of n states, plus an n×n transi*on probability matrix P.   At each step, we are in exactly one of the states.   For 1 ≤ i,j ≤ n, the matrix entry Pij tells us the probability of j being the next state, given we are currently in state i. Pii>0 is OK. i Pij j 4 Introduc)on to Informa)on Retrieval Sec. 21.2.1 Markov ch...
View Full Document

This document was uploaded on 02/26/2014.

Ask a homework question - tutors are online