# AdnFM: An Attentive DenseNet based Factorization Machine for CTR Prediction

arXiv: https://arxiv.org/abs/2012.10820 · HTML: https://mlsanity.com/paper/2012.10820/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2020.

## Built on by (most cited first, 2 of 2)

- [4G 5G Cell-level Multi-indicator Forecasting based on Dense-MLP](https://mlsanity.com/paper/2209.05989/) (2022)
- [From Features to Graphs: Exploring Graph Structures and Pairwise Interactions via GNNs](https://mlsanity.com/paper/2502.13471/) (2025)

## Abstract (arXiv)

In this paper, we consider the Click-Through-Rate (CTR) prediction problem. Factorization Machines and their variants consider pair-wise feature interactions, but normally we won't do high-order feature interactions using FM due to high time complexity. Given the success of deep neural networks (DNNs) in many fields, researchers have proposed several DNN-based models to learn high-order feature interactions. Multi-layer perceptrons (MLP) have been widely employed to learn reliable mappings from feature embeddings to final logits. In this paper, we aim to explore more about these high-order features interactions. However, high-order feature interaction deserves more attention and further development. Inspired by the great achievements of Densely Connected Convolutional Networks (DenseNet) in computer vision, we propose a novel model called Attentive DenseNet based Factorization Machines (AdnFM). AdnFM can extract more comprehensive deep features by using all the hidden layers from a feed-forward neural network as implicit high-order features, then selects dominant features via an attention mechanism. Also, high-order interactions in the implicit way using DNNs are more cost-efficient than in the explicit way, for example in FM. Extensive experiments on two real-world datasets show that the proposed model can effectively improve the performance of CTR prediction.