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An Efficient Deep Interaction Network for Click-Through Rate Prediction

Published: 25 August 2020 Publication History

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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HPCCT & BDAI '20: Proceedings of the 2020 4th High Performance Computing and Cluster Technologies Conference & 2020 3rd International Conference on Big Data and Artificial Intelligence
July 2020
276 pages
ISBN:9781450375603
DOI:10.1145/3409501
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • Xi'an Jiaotong-Liverpool University: Xi'an Jiaotong-Liverpool University

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Published: 25 August 2020

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  1. Neural network
  2. click-through rate
  3. feature interaction

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