《百面深度学习》试读 | 系列五：计算广告中的点击率预估问题
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导言
《百面深度学习》自上市以来，获得了众多读者的关注与支持，一直高居京东计算机与互联网类新书榜单的前列，大家的热情是我们精益求精的源源动力。为了更好地与大家进行分享与交流，我们从书中节选了几个关注度比较高的“热门“知识点，重新加以整理，内容涵盖推荐系统、计算广告、自然语言处理、计算机视觉、视频处理、生成式对抗网络等领域的相关知识，供大家试读。
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《百面深度》试读第五篇
[1] COVINGTON P, ADAMS J, SARGIN E. Deep neural networks for YouTube recommendations[C]//Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 2016: 191–198.
[2] CHENG H-T, KOC L, HARMSEN J, 等. Wide & deep learning for recommender systems[C]//Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. ACM, 2016: 7–10.
[3] JUAN Y, ZHUANG Y, CHIN W-S, 等. Field-aware factorization machines for CTR prediction[C]//Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 2016: 43–50.
[4] PAN J, XU J, RUIZ A L, 等. Field-weighted factorization machines for click-through rate prediction in display advertising[C]//Proceedings of the 2018 World Wide Web Conference. International World Wide Web Conferences Steering Committee, 2018: 1349–1357.
[5] GUO H, TANG R, YE Y, 等. DeepFM: A factorization-machine based neural network for CTR prediction[J]. arXiv preprint arXiv:1703.04247, 2017.
[6] ZHOU G, ZHU X, SONG C, 等. Deep interest network for click-through rate prediction[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2018: 1059–1068.
[7] LI L, CHU W, LANGFORD J, 等. A contextual-bandit approach to personalized news article recommendation[C]//Proceedings of the 19th International Conference on World Wide Web. ACM, 2010: 661–670.
[8] WALSH T J, SZITA I, DIUK C, 等. Exploring compact reinforcement-learning representations with linear regression[C]//Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence. AUAI Press, 2009: 591–598.
[9] AGRAWAL S, GOYAL N. Thompson sampling for contextual bandits with linear payoffs[C]//International Conference on Machine Learning. 2013: 127–135.
《百面深度学习》的试读系列的五篇已经放送完毕，和大家说再见啦~试读没有看饱，想要学习更多深度学习的算法和模型以及领域应用，可以即刻下单，看书中135道面试题结合问答，为你呈现深度学习领域的“百面”精彩！
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