SCouT: Scalable coupled matrix-tensor factorization - algorithm and discoveries

Abstract

How can we analyze very large real-world tensors where additional information is coupled with certain modes of tensors? Coupled matrix-tensor factorization is a useful tool to simultaneously analyze matrices and a tensor, and has been used for important applications including collaborative filtering, multi-way clustering, and link prediction. However, existing single machine or distributed algorithms for coupled matrix-tensor factorization do not scale for tensors with billions of elements in each mode. In this paper, we propose SCOUT, a large-scale coupled matrix-tensor factorization algorithm running on the distributed MAPREDUCE platform. By carefully reorganizing operations, and reusing intermediate data, SCOUT decomposes up to 100× larger tensors than existing methods, and shows linear scalability for order and machines while other methods are limited in scalability. We also apply SCOUT on real world tensors and discover interesting hidden patterns like seasonal spike, and steady attentions for healthy food on Yelp dataset containing user-business-yearmonth tensor and two coupled matrices.