  Predicting conversion rates (CVRs) in display advertising (e.g., predicting
the proportion of users who purchase an item (i.e., a conversion) after its
corresponding ad is clicked) is important when measuring the effects of ads
shown to users and to understanding the interests of the users. There is
generally a time delay (i.e., so-called {\it delayed feedback}) between the ad
click and conversion. Owing to the delayed feedback, samples that are converted
after an observation period may be treated as negative. To overcome this
drawback, CVR prediction assuming that the time delay follows an exponential
distribution has been proposed. In practice, however, there is no guarantee
that the delay is generated from the exponential distribution, and the best
distribution with which to represent the delay depends on the data. In this
paper, we propose a nonparametric delayed feedback model for CVR prediction
that represents the distribution of the time delay without assuming a
parametric distribution, such as an exponential or Weibull distribution.
Because the distribution of the time delay is modeled depending on the content
of an ad and the features of a user, various shapes of the distribution can be
represented potentially. In experiments, we show that the proposed model can
capture the distribution for the time delay on a synthetic dataset, even when
the distribution is complicated. Moreover, on a real dataset, we show that the
proposed model outperforms the existing method that assumes an exponential
distribution for the time delay in terms of conversion rate prediction.
