广告系统位置偏差的CTR模型优化方案( 八 )

[1] Chen, Jiawei, et al. "Bias and Debias in Recommender System: A Survey and Future Directions." arXiv preprint arXiv:2010.03240 (2020).
[2] Ca?amares, Rocío, and Pablo Castells. "Should I follow the crowd? A probabilistic analysis of the effectiveness of popularity in recommender systems." The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. 2018.
[3] Morik, Marco, et al. "Controlling fairness and bias in dynamic learning-to-rank." Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2020.
[4] 《KDD Cup 2020 Debiasing比赛冠军技术方案及在美团的实践》。
[5] Richardson, Matthew, Ewa Dominowska, and Robert Ragno. "Predicting clicks: estimating the click-through rate for new ads." Proceedings of the 16th international conference on World Wide Web. 2007.
[6] Rendle, Steffen. "Factorization machines." 2010 IEEE International Conference on Data Mining. IEEE, 2010.
[7] Juan, Yuchin, et al. "Field-aware factorization machines for CTR prediction." Proceedings of the 10th ACM conference on recommender systems. 2016.
[8] Chen, Tianqi, and Carlos Guestrin. "Xgboost: A scalable tree boosting system." Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. 2016.
[9] Ke, Guolin, et al. "Lightgbm: A highly efficient gradient boosting decision tree." Advances in neural information processing systems 30 (2017): 3146-3154.
[10] Cheng, Heng-Tze, et al. "Wide & deep learning for recommender systems." Proceedings of the 1st workshop on deep learning for recommender systems. 2016.
[11] Wang, Ruoxi, et al. "Deep & cross network for ad click predictions." Proceedings of the ADKDD'17. 2017. 1-7.
[12] Guo, Huifeng, et al. "DeepFM: a factorization-machine based neural network for CTR prediction." arXiv preprint arXiv:1703.04247 (2017).
[13] Lian, Jianxun, et al. "xdeepfm: Combining explicit and implicit feature interactions for recommender systems." Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2018.
[14] Zhou, Guorui, et al. "Deep interest network for click-through rate prediction." Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2018.
[15] Zhou, Guorui, et al. "Deep interest evolution network for click-through rate prediction." Proceedings of the AAAI conference on artificial intelligence. Vol. 33. No. 01. 2019.
[16] Feng, Yufei, et al. "Deep session interest network for click-through rate prediction." arXiv preprint arXiv:1905.06482 (2019).
[17] Ling, Xiaoliang, et al. "Model ensemble for click prediction in bing search ads." Proceedings of the 26th International Conference on World Wide Web Companion. 2017.
[18] Zhao, Zhe, et al. "Recommending what video to watch next: a multitask ranking system." Proceedings of the 13th ACM Conference on Recommender Systems. 2019.