AdnFM: An Attentive DenseNet based Factorization Machine for CTR Prediction

• URL: http://arxiv.org/abs/2012.10820v1
• Date: Sun, 20 Dec 2020 01:00:39 GMT
• Title: AdnFM: An Attentive DenseNet based Factorization Machine for CTR Prediction
• Authors: Kai Wang, Chunxu Shen, Wenye Ma
• Abstract summary: 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. Experiments on two real-world datasets show that the proposed model can effectively improve the performance of Click-Through-Rate prediction.
• Score: 11.958336595818267
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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