  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.
