In this paper related to factorization machine, the author compares factorization machine (FM) with SVM. As FM performs better than SVM, it's considered state of the art for sparse data. Why SVM is considered so important algorithm for sparse dataset? What features of SVM makes it superior for sparse data?
Support Vector Machines (SVM) represent data examples as points in space and tries to create a mapping with a wide as possible gap between the separate categories. The data examples closest to the gap are called "support vectors". Those support vectors define the SVM classifier.
If you are able to identify the support vectors, you can ignore all other data points and dimensions. Thus SVM can efficiently handle sparse data.