Learning similarity measures based on random walks

William W. Cohen

Abstract: We describe a novel learnable proximity measure based on personalized PageRank (also known as "random walk with reset"). Instead of introducing one weight per edge label, as in most prior work, we introduce one weight for each edge label sequence. We show that this approach is advantageous for a number of real-world tasks, including querying graph databases, recommendation tasks, and inference in large, noisy knowledge bases.