An Efficient Deep Interaction Network for Click-Through Rate Prediction
Authors: 
Xu Li, Xiang Ji, Li ZhangAuthors Info & Claims
Xu Li, Xiang Ji, Li ZhangAuthors Info & ClaimsPages 246 - 250
Abstract
Click-through rate (CTR) prediction is crucial for recommender systems and it plays a significant role in Internet companies such as Facebook and Alibaba, etc. How to effectively model the interactions among various original features usually decides the performance of the CTR predictor. In real scenarios, useful features interactions are always distributed sparsely and it is very difficult for deep neural network to learn them by gradient back-propagation mechanism under millions of parameters. In order to alleviate this dilemma, a deep interaction network is proposed in this paper, in which the 1-order, 2-order, 3-order, 4-order features interaction behavior are explicitly modelled. In addition, a convolutional based feature extractor for learning high order features combination is integrated in parallel. Finally the inference logits which indicates the user click probability of all orders are fused by learnable weights. Criteo public dataset is applied to evaluate the performance of the proposed approach and experimental result demonstrates that our deep interaction network outperforms the other state-of-the-art deep models such as FNN, DeepFM, FGCNN, etc, achieving 0.4534 logloss and 0.7645 AUC (area unde curve) over test dataset.
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Index Terms
- An Efficient Deep Interaction Network for Click-Through Rate Prediction
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Published In
July 2020
276 pages
ISBN:9781450375603
DOI:10.1145/3409501
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- Xi'an Jiaotong-Liverpool University: Xi'an Jiaotong-Liverpool University
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Association for Computing Machinery
New York, NY, United States
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Published: 25 August 2020
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