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深度学习在CTR预估中的应用

Deep Learning for Click-Through Rate Estimation

Weinan Zhang, Jiarui Qin, Wei Guo, Ruiming Tang, Xiuqiang He

Shanghai Jiao Tong University, Huawei Noah’s Ark Lab

https://arxiv.org/pdf/2104.10584.pdf

点击率预估在很多个性化在线服务中起着非常重要的作用，是一个重要模块，比如在线广告，推荐系统，网络搜索等。

自从2015年起，深度学习的成功开始用于提升点击率预估的效果，现在深度点击率模型已经广泛用于很多工业平台中。这篇综述会综合回顾点击率预估中的深度学习模型。首先回顾从浅层到深层点击率预估模型的转变，然后解释模型往深层发展趋势的必要性。

然后。简介ctr模型中显式特征交互模块。基于丰富的用户历史和大平台，介绍深层行为模型。最近兴起的自动设计深层点击率模型结构会简单介绍下。

多个域的类别数据示例如下

目标函数形式如下

点击率预估模型发展历史图示如下

单塔和双塔的ctr预估模型结构对比如下

几种典型的特征交互算子图示如下

用户行为建模图示如下

参考资料（文献列表）

Deepak Agarwal, Bo Long, Jonathan Traupman, Doris Xin, and Liang Zhang. Laser: A scalable response prediction platform for online advertising. In WSDM, pages 173–182, 2014.

Mathieu Blondel, Akinori Fujino, Naonori Ueda, and Masakazu Ishihata. Higher-order factorization machines. In NIPS, 2016.

Yin-Wen Chang, Cho-Jui Hsieh, KaiWei Chang, Michael Ringgaard, and Chih-Jen Lin. Training and testing low-degree polynomial data mappings via linear svm. Journal of Machine Learning Research, 11(4), 2010.

Shih-Kang Chao and Guang Cheng. A generalization of regularized dual averaging and its dynamics. CoRR, abs/1909.10072, 2019.

Qiwei Chen, Huan Zhao, Wei Li, Pipei Huang, and Wenwu Ou. Behavior sequence transformer for e-commerce recommendation in alibaba. In 1st DLP-KDD Workshop, pages 1–4, 2019.

Yifan Chen, Pengjie Ren, Yang Wang, and Maarten de Rijke. Bayesian personalized feature interaction selection for factorization machines. In SIGIR, pages 665–674, 2019.

Chen Cheng, Fen Xia, Tong Zhang, Irwin King, and Michael R Lyu. Gradient boosting factorization machines. In RecSys, pages 265–272. ACM, 2014.

Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, et al. Wide & deep learning for recommender systems. In 1st DLRS workshop, pages 7–10, 2016.

Weiyu Cheng, Yanyan Shen, and Linpeng Huang. Differentiable neural input search for recommender systems. CoRR, abs/2006.04466, 2020.

George Cybenko. Approximation by superpositions of a sigmoidal function. Mathematics of control, signals and systems, 2(4):303–314, 1989.

Haiyan Fan and Marshall Scott Poole. What is personalization? perspectives on the design and implementation of personalization in information systems. Journal of Organizational Computing and Electronic Commerce, 16(3-4):179–202, 2006.

Hui Fang, Danning Zhang, Yiheng Shu, and Guibing Guo. Deep learning for sequential recommendation: Algorithms, influential factors, and evaluations. TOIS, 39(1):1–42, 2020.

Yufei Feng, Fuyu Lv, Weichen Shen, Menghan Wang, Fei Sun, Yu Zhu, and Keping Yang. Deep session interest network for click-through rate prediction. In IJCAI, 2019.

Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. Deepfm: a factorizationmachine based neural network for ctr prediction. IJCAI, 2017.

Xiangnan He and Tat-Seng Chua. Neural factorization machines for sparse predictive analytics. In SIGIR, pages 355–364, 2017.

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, pages 770–778, 2016.

Jie Hu, Li Shen, and Gang Sun. Squeezeand-excitation networks. In CVPR, pages 7132–7141, 2018.

Tongwen Huang, Zhiqi Zhang, and Junlin Zhang. Fibinet: combining feature importance and bilinear feature interaction for click-through rate prediction. In RecSys, pages 169–177, 2019.

Eric Jang, Shixiang Gu, and Ben Poole. Categorical reparameterization with gumbel-softmax. In ICLR, 2017.

Manas R. Joglekar, Cong Li, Mei Chen, Taibai Xu, Xiaoming Wang, Jay K. Adams, Pranav Khaitan, Jiahui Liu, and Quoc V. Le. Neural input search for large scale recommendation models. In KDD, 2020.

Yuchin Juan, Yong Zhuang, Wei-Sheng Chin, and Chih-Jen Lin. Field-aware factorization machines for ctr prediction. In RecSys, pages 43–50, 2016.

Farhan Khawar, Xu Hang, Ruiming Tang, Bin Liu, Zhenguo Li, and Xiuqiang He. Autofeature: Searching for feature interactions and their architectures for click-through rate prediction. In CIKM, 2020.

Zekun Li, Zeyu Cui, Shu Wu, Xiaoyu Zhang, and Liang Wang. Fi-gnn: Modeling feature interactions via graph neural networks for ctr prediction. In CIKM, 2019.

Zeyu Li, Wei Cheng, Yang Chen, Haifeng Chen, and Wei Wang. Interpretable click-through rate prediction through hierarchical attention. In WSDM, 2020.

Jianxun Lian, Xiaohuan Zhou, Fuzheng Zhang, Zhongxia Chen, Xing Xie, and Guangzhong Sun. xdeepfm: Combining explicit and implicit feature interactions for recommender systems. In KDD, 2018.

Qiang Liu, Feng Yu, Shu Wu, and Liang Wang. A convolutional click prediction model. In CIKM, 2015.

Bin Liu, Ruiming Tang, Yingzhi Chen, Jinkai Yu, Huifeng Guo, and Yuzhou Zhang. Feature generation by convolutional neural network for click-through rate prediction. In WWW, pages 1119–1129, 2019.

Hanxiao Liu, Karen Simonyan, and Yiming Yang. DARTS: differentiable architecture search. In ICLR, 2019.

Bin Liu, Niannan Xue, Huifeng Guo, Ruiming Tang, Stefanos Zafeiriou, Xiuqiang He, and Zhenguo Li. Autogroup: Automatic feature grouping for modelling explicit high-order feature interactions in ctr prediction. In SIGIR, pages 199–208, 2020.

Bin Liu, Chenxu Zhu, Guilin Li, Weinan Zhang, Jincai Lai, Ruiming Tang, Xiuqiang He, Zhenguo Li, and Yong Yu. Autofis: Automatic feature interaction selection in factorization models for click-through rate prediction. In KDD, pages 2636–2645, 2020.

Haochen Liu, Xiangyu Zhao, Chong Wang, Xiaobing Liu, and Jiliang Tang. Automated embedding size search in deep recommender systems. In SIGIR, pages 2307–2316, 2020.

Siyi Liu, Chen Gao, Yihong Chen, Depeng Jin, and Yong Li. Learnable embedding sizes for recommender systems. CoRR, abs/2101.07577, 2021.

Junwei Pan, Jian Xu, Alfonso Lobos Ruiz, Wenliang Zhao, Shengjun Pan, Yu Sun, and Quan Lu. Fieldweighted factorization machines for click-through rate prediction in display advertising. In WWW, 2018.

Qi Pi, Weijie Bian, Guorui Zhou, Xiaoqiang Zhu, and Kun Gai. Practice on long sequential user behavior modeling for click-through rate prediction. In KDD, 2019.

Pi Qi, Xiaoqiang Zhu, Guorui Zhou, Yujing Zhang, Zhe Wang, Lejian Ren, Ying Fan, and Kun Gai. Search-based user interest modeling with lifelong sequential behavior data for click-through rate prediction. In KDD, 2020.

Jiarui Qin, W. Zhang, Xin Wu, Jiarui Jin, Yuchen Fang, and Y. Yu. User behavior retrieval for clickthrough rate prediction. In SIGIR, 2020.

Yanru Qu, Han Cai, Kan Ren, Weinan Zhang, Yong Yu, Ying Wen, and Jun Wang. Product-based neural networks for user response prediction. In ICDM, pages 1149–1154, 2016.

Yanru Qu, Bohui Fang, Weinan Zhang, Ruiming Tang, Minzhe Niu, Huifeng Guo, Yong Yu, and Xiuqiang He. Product-based neural networks for user response prediction over multi-field categorical data. TOIS, 37(1):1–35, 2018.

Kan Ren, Jiarui Qin, Yuchen Fang, Weinan Zhang, Lei Zheng, Weijie Bian, Guorui Zhou, Jian Xu, Yong Yu, Xiaoqiang Zhu, et al. Lifelong sequential modeling with personalized memorization for user response prediction. In SIGIR, 2019.

Steffen Rendle, Walid Krichene, Li Zhang, and John Anderson. Neural collaborative filtering vs. matrix factorization revisited. In RecSys, 2020.

Steffen Rendle. Factorization machines. In ICDM, 2010.

Matthew Richardson, Ewa Dominowska, and Robert Ragno. Predicting clicks: estimating the click-through rate for new ads. In WWW, 2007.

Ying Shan, T Ryan Hoens, Jian Jiao, Haijing Wang, Dong Yu, and JC Mao. Deep crossing: Webscale modeling without manually crafted combinatorial features. In KDD, pages 255–262, 2016.

Weiping Song, Chence Shi, Zhiping Xiao, Zhijian Duan, Yewen Xu, Ming Zhang, and Jian Tang. Autoint: Automatic feature interaction learning via selfattentive neural networks. In CIKM, 2019.

Qingquan Song, Dehua Cheng, Hanning Zhou, Jiyan Yang, Yuandong Tian, and Xia Hu. Towards automated neural interaction discovery for click-through rate prediction. In KDD, pages 945–955, 2020.

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. In NIPS, pages 5998–6008, 2017.

Ruoxi Wang, Bin Fu, Gang Fu, and Mingliang Wang. Deep & cross network for ad click predictions. In ADKDD, pages 1–7. 2017.

Ruoxi Wang, Rakesh Shivanna, Derek Z Cheng, Sagar Jain, Dong Lin, Lichan Hong, and Ed H Chi. Dcn-m: Improved deep & cross network for feature cross learning in web-scale learning to rank systems. arXiv preprint arXiv:2008.13535, 2020.

Jun Xiao, Hao Ye, Xiangnan He, Hanwang Zhang, Fei Wu, and Tat-Seng Chua. Attentional factorization machines: Learning the weight of feature interactions via attention networks. IJCAI, 2017.

Quanming Yao, Xiangning Chen, James T. Kwok, Yong Li, and Cho-Jui Hsieh. Efficient neural interaction function search for collaborative filtering. In WWW, pages 1660–1670, 2020.

Weinan Zhang, Tianming Du, and Jun Wang. Deep learning over multi-field categorical data: A case study on user response prediction. ECIR, 2016.

Pengyu Zhao, Kecheng Xiao, Yuanxing Zhang, Kaigui Bian, and Wei Yan. AMER: automatic behavior modeling and interaction exploration in recommender system. CoRR, abs/2006.05933, 2020.

Xiangyu Zhao, Haochen Liu, Hui Liu, Jiliang Tang, Weiwei Guo, Jun Shi, Sida Wang, Huiji Gao, and Bo Long. Memory-efficient embedding for recommendations. CoRR, abs/2006.14827, 2020.

Xiangyu Zhao, Chong Wang, Ming Chen, Xudong Zheng, Xiaobing Liu, and Jiliang Tang. Autoemb: Automated embedding dimensionality search in streaming recommendations. CoRR, abs/2002.11252, 2020.

Guorui Zhou, Xiaoqiang Zhu, Chenru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai. Deep interest network for clickthrough rate prediction. In KDD, 2018.

Guorui Zhou, Na Mou, Ying Fan, Qi Pi, Weijie Bian, Chang Zhou, Xiaoqiang Zhu, and Kun Gai. Deep interest evolution network for click-through rate prediction. In AAAI, volume 33, pages 5941–5948, 2019.

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深度学习在CTR预估中的应用

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