The emergence of real-time auction in online advertising has drawn huge
attention of modeling the market competition, i.e., bid landscape forecasting.
The problem is formulated as to forecast the probability distribution of market
price for each ad auction. With the consideration of the censorship issue which
is caused by the second-price auction mechanism, many researchers have devoted
their efforts on bid landscape forecasting by incorporating survival analysis
from medical research field. However, most existing solutions mainly focus on
either counting-based statistics of the segmented sample clusters, or learning
a parameterized model based on some heuristic assumptions of distribution
forms. Moreover, they neither consider the sequential patterns of the feature
over the price space. In order to capture more sophisticated yet flexible
patterns at fine-grained level of the data, we propose a Deep Landscape
Forecasting (DLF) model which combines deep learning for probability
distribution forecasting and survival analysis for censorship handling.
Specifically, we utilize a recurrent neural network to flexibly model the
conditional winning probability w.r.t. each bid price. Then we conduct the bid
landscape forecasting through probability chain rule with strict mathematical
derivations. And, in an end-to-end manner, we optimize the model by minimizing
two negative likelihood losses with comprehensive motivations. Without any
specific assumption for the distribution form of bid landscape, our model shows
great advantages over previous works on fitting various sophisticated market
price distributions. In the experiments over two large-scale real-world
datasets, our model significantly outperforms the state-of-the-art solutions
under various metrics.

在线广告实时拍卖引起了人们对市场竞争建模的巨大关注，即出价预测。本文提出了一个结合深度学习和生存分析处理审查问题的深度景观预测模型，通过循环神经网络灵活地建模每个出价价格条件下的获胜概率，然后通过概率链规则进行出价预测。在两个大规模实际数据集上的实验中，我们的模型在各种指标下明显优于现有的最先进的解决方案。