ABSTRACT
Click-Through Rate prediction is an important task in recommender systems, which aims to estimate the probability of a user to click on a given item. Recently, many deep models have been proposed to learn low-order and high-order feature interactions from original features. However, since useful interactions are always sparse, it is difficult for DNN to learn them effectively under a large number of parameters. In real scenarios, artificial features are able to improve the performance of deep models (such as Wide & Deep Learning), but feature engineering is expensive and requires domain knowledge, making it impractical in different scenarios. Therefore, it is necessary to augment feature space automatically. In this paper, We propose a novel Feature Generation by Convolutional Neural Network (FGCNN) model with two components: Feature Generation and Deep Classifier. Feature Generation leverages the strength of CNN to generate local patterns and recombine them to generate new features. Deep Classifier adopts the structure of IPNN to learn interactions from the augmented feature space. Experimental results on three large-scale datasets show that FGCNN significantly outperforms nine state-of-the-art models. Moreover, when applying some state-of-the-art models as Deep Classifier, better performance is always achieved, showing the great compatibility of our FGCNN model. This work explores a novel direction for CTR predictions: it is quite useful to reduce the learning difficulties of DNN by automatically identifying important features.
- Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural Machine Translation by Jointly Learning to Align and Translate. Computer Science (2014).Google Scholar
- Antoine Bordes, Y-Lan Boureau, and Jason Weston. 2016. Learning end-to-end goal-oriented dialog. arXiv preprint arXiv:1605.07683(2016).Google Scholar
- Patrick PK Chan, Xian Hu, Lili Zhao, Daniel S Yeung, Dapeng Liu, and Lei Xiao. 2018. Convolutional Neural Networks based Click-Through Rate Prediction with Multiple Feature Sequences.. In IJCAI. 2007-2013. Google ScholarDigital Library
- Yin Wen Chang, Cho Jui Hsieh, Kai Wei Chang, Michael Ringgaard, and Chih Jen Lin. 2010. Training and Testing Low-degree Polynomial Data Mappings via Linear SVM. Journal of Machine Learning Research 11, 11 (2010), 1471-1490. Google ScholarDigital Library
- Junxuan Chen, Baigui Sun, Hao Li, Hongtao Lu, and Xian Sheng Hua. 2016. Deep CTR Prediction in Display Advertising. (2016), 811-820. Google ScholarDigital Library
- Tianqi Chen and Carlos Guestrin. 2016. XGBoost:A Scalable Tree Boosting System. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 785-794. Google ScholarDigital Library
- Hengtze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Deepak Chandra, Hrishi Aradhye, Glen Anderson, Greg S Corrado, Wei Chai, Mustafa Ispir, 2016. Wide & Deep Learning for Recommender Systems. conference on recommender systems(2016), 7-10. Google ScholarDigital Library
- Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM conference on recommender systems. ACM, 191-198. Google ScholarDigital Library
- Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017. Deepfm: a factorization-machine based neural network for ctr prediction. arXiv preprint arXiv:1703.04247(2017).Google Scholar
- Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, Xiuqiang He, and Zhenhua Dong. 2018. DeepFM: An End-to-End Wide & Deep Learning Framework for CTR Prediction. (2018).Google Scholar
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2015. Deep Residual Learning for Image Recognition. (2015), 770-778.Google Scholar
- Xiangnan He and TatSeng Chua. 2017. Neural Factorization Machines for Sparse Predictive Analytics. (2017), 355-364. Google ScholarDigital Library
- Xinran He, Junfeng Pan, Ou Jin, Tianbing Xu, Bo Liu, Tao Xu, Yanxin Shi, Antoine Atallah, Ralf Herbrich, and Stuart Bowers. 2014. Practical Lessons from Predicting Clicks on Ads at Facebook. In Eighth International Workshop on Data Mining for Online Advertising. 1-9. Google ScholarDigital Library
- Gao Huang, Zhuang Liu, Van Der Maaten Laurens, and Kilian Q Weinberger. 2016. Densely Connected Convolutional Networks. (2016), 2261-2269.Google Scholar
- Sergey Ioffe and Christian Szegedy. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167(2015).Google Scholar
- Yuchin Juan, Yong Zhuang, Wei Sheng Chin, and Chih Jen Lin. 2016. Field-aware Factorization Machines for CTR Prediction. In ACM Conference on Recommender Systems. 43-50. Google ScholarDigital Library
- Kuang Chih Lee, Burkay Orten, Ali Dasdan, and Wentong Li. 2012. Estimating conversion rate in display advertising from past erformance data. In Acm Sigkdd International Conference on Knowledge Discovery & Data Mining. 768-776. Google ScholarDigital Library
- Kuang Chih Lee, Burkay Orten, Ali Dasdan, and Wentong Li. 2012. Estimating conversion rate in display advertising from past erformance data. In Acm Sigkdd International Conference on Knowledge Discovery & Data Mining. 768-776. Google ScholarDigital Library
- Jianxun Lian, Xiaohuan Zhou, Fuzheng Zhang, Zhongxia Chen, Xing Xie, and Guangzhong Sun. 2018. xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems. arXiv preprint arXiv:1803.05170(2018).Google Scholar
- 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. ACM, 1743-1746. Google ScholarDigital Library
- H Brendan McMahan, Gary Holt, David Sculley, Michael Young, Dietmar Ebner, Julian Grady, Lan Nie, Todd Phillips, Eugene Davydov, Daniel Golovin, 2013. Ad click prediction: a view from the trenches. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 1222-1230. Google ScholarDigital Library
- 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 2016 IEEE 16th International Conference on Data Mining (ICDM). IEEE, 1149-1154.Google ScholarCross Ref
- Yanru Qu, Bohui Fang, Weinan Zhang, Ruiming Tang, Minzhe Niu, Huifeng Guo, Yong Yu, and Xiuqiang He. 2018. Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data. arXiv preprint arXiv:1807.00311(2018).Google Scholar
- Alec Radford, Rafal Jozefowicz, and Ilya Sutskever. 2017. Learning to Generate Reviews and Discovering Sentiment. (2017).Google Scholar
- Kan Ren, Weinan Zhang, Yifei Rong, Haifeng Zhang, Yong Yu, and Jun Wang. 2016. User Response Learning for Directly Optimizing Campaign Performance in Display Advertising. (2016), 679-688. Google ScholarDigital Library
- Steffen Rendle. 2010. Factorization machines. In Data Mining (ICDM), 2010 IEEE 10th International Conference on. IEEE, 995-1000. Google ScholarDigital Library
- Steffen Rendle. 2011. Factorization Machines. In IEEE International Conference on Data Mining. 995-1000. Google ScholarDigital Library
- Matthew Richardson, Ewa Dominowska, and Robert Ragno. 2007. Predicting clicks:estimating the click-through rate for new ads. In International Conference on World Wide Web. 521-530. Google ScholarDigital Library
- Shai Shalevshwartz, Ohad Shamir, and Shaked Shammah. 2017. Failures of Gradient-Based Deep Learning. (2017). Google ScholarDigital Library
- Ying Shan, T. Ryan Hoens, Jian Jiao, Haijing Wang, Dong Yu, and J. C. Mao. 2016. Deep Crossing:Web-Scale Modeling without Manually Crafted Combinatorial Features. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 255-262. Google ScholarDigital Library
- Guangzhong Sun, Guangzhong Sun, Guangzhong Sun, Guangzhong Sun, Guangzhong Sun, and Guangzhong Sun. 2017. Practical Lessons for Job Recommendations in the Cold-Start Scenario. In Recommender Systems Challenge. 4.Google Scholar
- Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alex Alemi. 2016. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. (2016).Google Scholar
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention Is All You Need. (2017). Google ScholarDigital Library
- Ruoxi Wang, Bin Fu, Gang Fu, and Mingliang Wang. 2017. Deep & cross network for ad click predictions. In Proceedings of the ADKDD'17. ACM, 12. Google ScholarDigital Library
- 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. arXiv preprint arXiv:1708.04617(2017).Google Scholar
- Saining Xie, Ross Girshick, Piotr Dollar, Zhuowen Tu, and Kaiming He. 2017. Aggregated Residual Transformations for Deep Neural Networks. In IEEE Conference on Computer Vision and Pattern Recognition. 5987-5995.Google Scholar
- Shuai Zhang, Lina Yao, and Aixin Sun. 2017. Deep learning based recommender system: A survey and new perspectives. arXiv preprint arXiv:1707.07435(2017).Google Scholar
- Weinan Zhang, Tianming Du, and Jun Wang. 2016. Deep Learning over Multi-field Categorical Data. (2016).Google Scholar
- Weinan Zhang, Tianming Du, and Jun Wang. 2016. Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction. (2016).Google Scholar
- Guorui Zhou, Xiaoqiang Zhu, Chenru 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. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 1059-1068. Google ScholarDigital Library
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