The model is improved based on FM. In fact, NN is superimposed on FM.
- Various sparse features are input
- Next, go through the FM layer to get the implicit vector of features in the FM model
- The input layer Z: z = (W0, Z1, Z2,…, Zn) of NN can be constructed through the hidden vector, and Zi = (WI, VI1, vi2,…, Vik), where wi is the hidden vector corresponding to the first-order weight VI in FM.
- Finally, the NN layer is connected to get the final result.
- Loss function: l (y, ˆ y) = −ylog ˆ y − (1 − y)log(1 − ˆ y)
The biggest advantage of FNN is that it does not need Feature Engineering, and its features are constructed by hidden vectors.