![]() Only a small modification of the computational graph of an FM model. Our technique preserves fast training and inference, and requires Its well-known approximation power, availability in software libraries, andĮfficiency. Known to have strong approximation power, and offer the B-Spline basis due to Hence, to improve model accuracy we advocate the use of functions Learns segmentized functions of the numerical feature spanned by the set ofįunctions of one's choice, namely, the spanning coefficients vary between From this perspective, we show that our technique yields a model that Remaining fields are assigned some given constants, which we refer to as the Namely, functions from a field's value to the real numbers, assuming the We view factorization machines as approximators of segmentized functions, Theoretically-justified way to incorporate numerical features into FM variantsīy encoding them into a vector of function values for a set of functions of In this work, we provide a systematic and Typically incorporated using a scalar transformation or binning, which can beĮither learned or chosen a-priori. Incorporating numerical columns poses a challenge, and they are Systems are trained on tabular data with both numerical and categoricalĬolumns. Model accuracy and low computational costs for training and inference. ![]() Download a PDF of the paper titled Basis Function Encoding of Numerical Features in Factorization Machines for Improved Accuracy, by Alex Shtoff and Elie Abboud and Rotem Stram and Oren Somekh Download PDF Abstract: Factorization machine (FM) variants are widely used for large scale real-timeĬontent recommendation systems, since they offer an excellent balance between
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