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.
References
[1]
Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, Rohan Anil, Zakaria Haque, Lichan Hong, Vihan Jain, Xiaobing Liu, and Hemal Shah. 2016. Wide & Deep Learning for Recommender Systems. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems (DLRS 2016). ACM, New York, NY, USA, 7--10. https://doi.org/10.1145/2988450.2988454
[2]
Yufei Feng, Fuyu Lv, Weichen Shen, Menghan Wang, Fei Sun, Yu Zhu, and Keping Yang. 2019. Deep Session Interest Network for Click-Through Rate Prediction. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10-16, 2019, Sarit Kraus (Ed.). ijcai.org, 2301--2307. https://doi.org/10.24963/ijcai.2019/319
[3]
Kun Gai, Xiaoqiang Zhu, Han Li, Kai Liu, and Zhe Wang. 2017. Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction. arXiv:stat.ML/1704.05194
[4]
Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction, See [12], 1725--1731. https://doi.org/10.24963/ijcai.2017/239
[5]
Yike Guo and Faisal Farooq (Eds.). 2018. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018, London, UK, August 19-23, 2018. ACM. https://doi.org/10.1145/3219819
[6]
Xiangnan He and Tat-Seng Chua. 2017. Neural Factorization Machines for Sparse Predictive Analytics. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Shinjuku, Tokyo, Japan, August 7-11, 2017, Noriko Kando, Tetsuya Sakai, Hideo Joho, Hang Li, Arjen P. de Vries, and Ryen W. White (Eds.). ACM, 355--364. https://doi.org/10.1145/3077136.3080777
[7]
Tongwen Huang, Zhiqi Zhang, and Junlin Zhang. 2019. FiB-iNET: combining feature importance and bilinear feature interaction for click-through rate prediction. In Proceedings of the 13th ACM Conference on Recommender Systems, RecSys 2019, Copenhagen, Denmark, September 16-20, 2019, Toine Bogers, Alan Said, Peter Brusilovsky, and Domonkos Tikk (Eds.). ACM, 169--177. https://doi.org/10.1145/3298689.3347043
[8]
Jianxun Lian, Xiaohuan Zhou, Fuzheng Zhang, Zhongxia Chen, Xing Xie, and Guangzhong Sun. 2018. xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems, See [5], 1754--1763. https://doi.org/10.1145/3219819.3220023
[9]
Bin Liu, Ruiming Tang, Yingzhi Chen, Jinkai Yu, Huifeng Guo, and Yuzhou Zhang. 2019. Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction. In The World Wide Web Conference, WWW 2019, San Francisco, CA, USA, May 13-17, 2019, Ling Liu, Ryen W. White, Amin Mantrach, Fabrizio Silvestri, Julian J. McAuley, Ricardo Baeza-Yates, and Leila Zia (Eds.). ACM, 1119--1129. https://doi.org/10.1145/3308558.3313497
[10]
Qiang Liu, Feng Yu, Shu Wu, and Liang Wang. 2015. A Convolutional Click Prediction Model. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management (CIKM '15). ACM, New York, NY, USA, 1743--1746. https://doi.org/10.1145/2806416.2806603
[11]
Yanru Qu, Han Cai, Kan Ren, Weinan Zhang, Yong Yu, Ying Wen, and Jun Wang. 2016. Product-Based Neural Networks for User Response Prediction. In IEEE 16th International Conference on Data Mining, ICDM 2016, December 12-15, 2016, Barcelona, Spain, Francesco Bonchi, Josep Domingo-Ferrer, Ricardo Baeza-Yates, Zhi-Hua Zhou, and Xindong Wu (Eds.). IEEE Computer Society, 1149--1154. https://doi.org/10.1109/ICDM.2016.0151
[12]
Carles Sierra (Ed.). 2017. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, August 19-25, 2017. ijcai.org. http://www.ijcai.org/Proceedings/2017/
[13]
Weiping Song, Chence Shi, Zhiping Xiao, Zhijian Duan, Yewen Xu, Ming Zhang, and Jian Tang. 2019. AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019, Beijing, China, November 3-7, 2019, Wenwu Zhu, Dacheng Tao, Xueqi Cheng, Peng Cui, Elke A. Rundensteiner, David Carmel, Qi He, and Jeffrey Xu Yu (Eds.). ACM, 1161--1170. https://doi.org/10.1145/3357384.3357925
[14]
Ruoxi Wang, Bin Fu, Gang Fu, and Mingliang Wang. 2017. Deep & Cross Network for Ad Click Predictions. In Proceedings of the ADKDD'17, Halifax, NS, Canada, August 13--17, 2017. ACM, 12:1--12:7. https://doi.org/10.1145/3124749.3124754
[15]
Jun Xiao, Hao Ye, Xiangnan He, Hanwang Zhang, Fei Wu, and Tat-Seng Chua. 2017. Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks, See [12], 3119--3125. https://doi.org/10.24963/ijcai.2017/435
[16]
Yi Yang, Baile Xu, Shaofeng Shen, Furao Shen, and Jian Zhao. 2020. Operation-aware Neural Networks for user response prediction. Neural Networks 121 (Jan 2020), 161--168. https://doi.org/10.1016/j.neunet.2019.09.020
[17]
Weinan Zhang, Tianming Du, and Jun Wang. 2016. Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction. Advances in Information Retrieval (2016), 45--57. https://doi.org/10.1007/978-3-319-30671-1_4
[18]
Guorui Zhou, Na Mou, Ying Fan, Qi Pi, Weijie Bian, Chang Zhou, Xiaoqiang Zhu, and Kun Gai. 2019. Deep Interest Evolution Network for Click-Through Rate Prediction. Proceedings of the AAAI Conference on Artificial Intelligence 33 (Jul 2019), 5941--5948. https://doi.org/10.1609/aaai.v33i01.33015941
[19]
Guorui Zhou, Xiaoqiang Zhu, Chengru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai. 2018. Deep Interest Network for Click-Through Rate Prediction, See [5], 1059--1068. https://doi.org/10.1145/3219819.3219823
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
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Published: 25 August 2020
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