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.Comment: KDD 2019. The reproducible code and dataset link is
https://github.com/rk2900/DL