AdnFM: An Attentive DenseNet based Factorization Machine for CTR Prediction
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.
AdnFM：基于DenseNet的细心分解机，可用于CTR预测
在本文中，我们考虑了点击率（CTR）预测问题。分解机及其变体考虑了成对特征交互，但是由于时间复杂度高，通常我们不会使用FM进行高阶特征交互。.. 鉴于深度神经网络（DNN）在许多领域都取得了成功，研究人员提出了几种基于DNN的模型来学习高阶特征相互作用。多层感知器（MLP）已被广泛用于学习从特征嵌入到最终logit的可靠映射。在本文中，我们旨在探索更多关于这些高阶特征相互作用的信息。但是，高阶特征交互值得更多关注和进一步发展。受到密集连接卷积网络（DenseNet）在计算机视觉中取得的巨大成就的启发，我们提出了一种新模型，称为基于注意力DenseNet的分解机（AdnFM）。AdnFM可以通过将前馈神经网络中的所有隐藏层用作隐式高阶特征来提取更全面的深度特征，然后通过注意力机制选择优势特征。同样，使用隐含神经网络以隐式方式进行的高阶交互比显式方式（例如在FM中）具有更高的成本效益。在两个真实数据集上的大量实验表明，该模型可以有效提高CTR预测的性能。 （阅读更多）
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