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How can we extract hidden relations from a tensor and a matrix data simultaneously in a fast, accurate, and scalable way? Coupled matrix-tensor factorization (CMTF) is an important tool for this purpose. 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 S 3 CMTF, a fast, accurate, and scalable CMTF method. In contrast to previous methods which do not handle large sparse tensors and are not parallelizable, S 3 CMTF provides parallel sparse CMTF by carefully deriving gradient update rules. S 3 CMTF asynchronously updates partial gradients without expensive locking. We show that our method is guaranteed to converge to a quality solution theoretically and empirically. S 3 CMTF further boosts the performance by carefully storing intermediate computation and reusing them. We theoretically and empirically show that S 3 CMTF is the fastest, outperforming existing methods. Experimental results show that S 3 CMTF is up to 930× faster than existing methods while providing the best accuracy. S 3 CMTF shows linear scalability on the number of data entries and the number of cores. In addition, we apply S 3 CMTF to Yelp rating tensor data coupled with 3 additional matrices to discover interesting patterns.

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