Fast, Accurate, and Scalable Method for Sparse Coupled Matrix-Tensor Factorization

TL;DR

This paper introduces S3CMTF, a novel method for coupled matrix-tensor factorization that is faster, more accurate, and more scalable than existing approaches, effectively handling large, sparse real-world data.

Contribution

The paper presents S3CMTF, a new scalable and accurate CMTF algorithm that exploits data sparsity and parallel processing, outperforming prior methods in speed and accuracy.

Findings

S3CMTF is 11-43 times faster than existing methods.

S3CMTF achieves 2.1-4.1 times higher accuracy.

The method scales linearly with data size and number of cores.

Abstract

How can we capture the hidden properties from a tensor and a matrix data simultaneously in a fast, accurate, and scalable way? Coupled matrix-tensor factorization (CMTF) is a major tool to extract latent factors from a tensor and matrices at once. Designing an accurate and efficient CMTF method has become more crucial as the size and dimension of real-world data are growing explosively. However, existing methods for CMTF suffer from lack of accuracy, slow running time, and limited scalability. In this paper, we propose S3CMTF, a fast, accurate, and scalable CMTF method. S3CMTF achieves high speed by exploiting the sparsity of real-world tensors, and high accuracy by capturing inter-relations between factors. Also, S3CMTF accomplishes additional speed-up by lock-free parallel SGD update for multi-core shared memory systems. We present two methods, S3CMTF-naive and S3CMTF-opt. S3CMTF-naive is a basic version of S3CMTF, and S3CMTF-opt improves its speed by exploiting intermediate data. We theoretically and empirically show that S3CMTF is the fastest, outperforming existing methods. Experimental results show that S3CMTF is 11~43 times faster, and 2.1~4.1 times more accurate than existing methods. S3CMTF shows linear scalability on the number of data entries and the number of cores. In addition, we apply S3CMTF to Yelp recommendation tensor data coupled with 3 additional matrices to discover interesting properties.