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 predictions of recommenders. The wide model is a generalized linear model of features together with their transforms. The deep model is a series of 5 hidden MLP layers of 1024 neurons each beginning with a dense embedding of features. The architecture is presented in Figure 1.
Figure 1. The architecture of the Wide & Deep model.
This model was trained using script available on NGC and in GitHub repo.
The following datasets were used to train this model:
Performance numbers for this model are available in NGC.