Wide & Deep Recommender model.
Wide & Deep refers to a class of networks that use the output of two parts working in parallel - wide model and deep model - to make a binary prediction of CTR. The wide model is a linear model of features together with their transforms. The deep model is a series of 5 hidden MLP layers of 1024 neurons. The model can handle both numerical continuous features as well as categorical features represented as dense embeddings. The architecture of the model is presented in Figure 1.
Figure 1. The architecture of the Wide & Deep model.
The following datasets were used to train this model:
Performance numbers for this model are available in NGC