Description
Publisher
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Model Overview
Wide & Deep Recommender model.
Model Architecture
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.
Training
This model was trained using script available on NGC and in GitHub repo.
Dataset
The following datasets were used to train this model:
- Outbrain - Dataset containing a sample of users’ page views and clicks, as observed on multiple publisher sites in the United States between 14-June-2016 and 28-June-2016. Each viewed page or clicked recommendation is further accompanied by some semantic attributes of those documents.
Performance
Performance numbers for this model are available in NGC.
References
License
This model was trained using open-source software available in Deep Learning Examples repository. For terms of use, please refer to the license of the script and the datasets the model was derived from.