Deep & Cross Network (DCN) is a neural network architecture developed by Google that combines a cross network with a deep network to efficiently learn explicit and implicit feature interactions. The original DCN was introduced in 2017, with an improved version, DCN v2, released in 2020. ## Architecture * Cross Network: takes the input embedding vector and computes cross-products with itself, each layer increases the polynomial degree by one. The input dimension is maintained throughout the network. * Deep Network: MLP with [[ReLU]] activation function * Combination: the outputs are concatenated and passed through a final dense layer to produce predictions (usually probability) ![[Pasted image 20250303082436.png]] ### DCN v2 Improvements * Replaces the vector-based cross operation with the matrix-based using low rank decomposition * Uses Mixture of Experts to decompose the learned matrix into sub-spaces, which are then aggregated with gates ![[Pasted image 20250303082534.png]] == ## Advantages - Automatically learns feature interactions without manual feature engineering - Lower computational complexity than comparable models ## Disadvantages - Original cross network limited in the types of interactions it can learn - Hyperparameter tuning can be challenging ## Links - [Original DCN Paper (2017)](https://arxiv.org/abs/1708.05123) - [DCN v2 Paper (2020)](https://arxiv.org/abs/2008.13535) - [TensorFlow Implementation](https://www.tensorflow.org/recommenders/examples/dcn)