I've noticed lately that a lot of people are developing tensor equivalents of many methods (tensor factorization, tensor kernels, tensors for topic modeling, etc) I'm wondering, why is the world suddenly fascinated with tensors? Are there recent papers/ standard results that are particularly surprising, that brought about this? Is it computationally a lot cheaper than previously suspected?

I'm not being glib, I sincerely am interested, and if there are any pointers to papers about this, I'd love to read them.

Tensors often offer more natural representations of data, e.g., consider video, which consists of obviously correlated images over time. You can turn this into a matrix, but it's just not natural or intuitive (what does a factorization of some matrix-representation of video mean?).

Tensors are trending for several reasons: 


our understanding of multilinear algebra is improving rapidly, specifically in various types of factorizations, which in turn helps us to identify new potential applications (e.g., multiway component analysis)
software tools are emerging (e.g., Tensorlab) and are being welcomed
Big Data applications can often be solved using tensors, for example recommender systems, and Big Data itself is hot
increases in computational power, as some tensor operations can be hefty (this is also one of the major reasons why deep learning is so popular now)

