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Although kernel methods efficiently use feature combinations without computing them directly, they do not scale well with the size of the training dataset. Factorization machines (FMs) and related models, on the other hand, enable feature combinations efficiently, but their optimization generally requires solving a non-convex problem. We present random feature maps for the itemset kernel, which uses feature combinations, and includes the ANOVA kernel, the all-subsets kernel, and the standarddoi:10.1609/aaai.v33i01.33013199 fatcat:4yhvhnomrjcqdia4ya73ut3cn4