DLRM implements a deep learning recommendation model for personalization and recommendation systems. The model accepts dense features (floating-point vectors) and sparse features (indices into embedding tables). It processes inputs through multi-layer perceptron networks and applies operators such as Sum, Dot, and Cat to embedding vectors before producing click probability predictions.