Andrei M Raigorodskii

Learning Supervised PageRank with Gradient-Based and Gradient-Free Optimization Methods

In this paper, we consider a non-convex loss-minimization problem of learning Supervised PageRank models, which can account for features of nodes and edges. We propose gradient-based and random gradient-free methods to solve this problem. Our algorithms are based on the concept of an inexact oracle…