Efficient processing of recommendation algorithms on a single-machine-based graph engine

Abstract

The wide use of recommendation systems includes more users and items in system operations, leading to a significant increase in the size of related datasets. However, recommendation algorithms on existing single-machine-based graph engines have been developed without considering the important characteristics of recommendation datasets, i.e., huge size and power-law degree distribution. In this paper, we address how to realize efficient graph- and matrix-factorization-based recommendation algorithms, handling recommendation datasets on RealGraph, a state-of-the-art single-machine-based graph engine. Through extensive experiments, we demonstrate that our recommendation algorithms on RealGraph universally and consistently outperform the algorithms on other graph engines over all datasets up to 34 times.