Feature Interaction based Neural Network for Click-Through Rate Prediction

• URL: http://arxiv.org/abs/2006.05312v1
• Date: Sun, 7 Jun 2020 03:53:24 GMT
• Title: Feature Interaction based Neural Network for Click-Through Rate Prediction
• Authors: Dafang Zou and Leiming Zhang and Jiafa Mao and Weiguo Sheng
• Abstract summary: We propose a Feature Interaction based Neural Network (FINN) which is able to model feature interaction via a 3-dimention relation tensor. We show that our deep FINN model outperforms other state-of-the-art deep models such as PNN and DeepFM. It also indicates that our models can effectively learn the feature interactions, and achieve better performances in real-world datasets.
• Score: 5.095988654970358
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Click-Through Rate (CTR) prediction is one of the most important and challenging in calculating advertisements and recommendation systems. To build a machine learning system with these data, it is important to properly model the interaction among features. However, many current works calculate the feature interactions in a simple way such as inner product and element-wise product. This paper aims to fully utilize the information between features and improve the performance of deep neural networks in the CTR prediction task. In this paper, we propose a Feature Interaction based Neural Network (FINN) which is able to model feature interaction via a 3-dimention relation tensor. FINN provides representations for the feature interactions on the the bottom layer and the non-linearity of neural network in modelling higher-order feature interactions. We evaluate our models on CTR prediction tasks compared with classical baselines and show that our deep FINN model outperforms other state-of-the-art deep models such as PNN and DeepFM. Evaluation results demonstrate that feature interaction contains significant information for better CTR prediction. It also indicates that our models can effectively learn the feature interactions, and achieve better performances in real-world datasets.

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