Laboratory of Structural Methods of Data Analysis in Predictive
Modeling Moscow Institute of Physics and Technology
Random walk based web page ranking functions learning with gradient-free optimization methods
In this paper we consider a problem of web page relevance to a search query. We are working in the framework called Semi-Supervised PageRank which can account for some properties which are not considered by classical approaches such as PageRank and BrowseRank algorithms. We introduce a graphical parametric model for web pages ranking. The goal is to identify the unknown parameters using the information about page relevance to a number of queries given by some experts (assessors). The resulting problem is formulated as an optimization one. Due to hidden huge dimension of the last problem we use random gradient-free methods to solve it. We prove the convergence theorem and give the number of arithmetic operations which is needed to solve it with a given accuracy.

Авторы: Dvurechensky Pavel , Gasnikov Alexander , Максим Жуковский

Дата: 27 декабря 2014

Год: 2014

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Huge-scale problems

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