Аннотация:
In this talk 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 develop random gradient-free methods with oracle error 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. This is a joint work with A. Gasnikov and M. Zhukovskii.
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