Programmatic advertising is the automated process of selling and buying online advertising space in real time, commonly referred to as the real-time bidding. Successful collaboration in real time bidding requires coordinated work of several processes, in which modern machine learning approaches play the crucial role. One of these is the modeling of the market price, which can in later stages help with identifying the optimal bid for a given advertising space.
We divide the algorithms for modeling the market price into two groups, the algorithms that model the entire probability distribution of the market price and the pointwise algorithms that predict the probability of winning only at a given bid value. In this work we have implemented and experimentally evaluated several algorithms from both groups. We have also proposed a new method for evaluating the predicted probability distributions that compares algorithms based on the generated reference probability distribution and the Kullback-Liebler divergence measure.
Our experiments show that algorithms that predict the entire probability distribution preform much better. Moreover, this type of algorithms require less time for the inference process than the pointwise algorithms.