The Deep Learning Recommendation Model (DLRM) is a recommendation model designed to make use of both categorical and numerical inputs.
DLRM accepts two types of features: categorical and numerical. For each categorical feature, an embedding table is used to provide dense representation to each unique value. The dense features enter the model and are transformed by a simple neural network referred to as "bottom MLP".
This part of the network consists of a series of linear layers with ReLU activations. The output of the bottom MLP and the embedding vectors are then fed into the "dot interaction" operation. The output of "dot interaction" is then concatenated with the features resulting from bottom MLP and fed into the "top MLP" which is a series of dense layers with activations. The model outputs a single number which can be interpreted as a likelihood of a certain user clicking an ad.
Figure 1. The architecture of DLRM.
The following datasets were used to train this model:
- Criteo 1TB - Dataset containing feature values and click feedback for millions of display ads. Its purpose is to benchmark algorithms for clickthrough rate (CTR) prediction.
Performance numbers for this model are available in NGC.