A LSTM-Hawkes hybrid model for posterior click distribution forecast in the advertising network environment.

ABSTRACT: In the field of advertising technology, it is a key task to forecast posterior click distribution since 66% of advertising transactions depend on cost per click model. However, due to the General Data Protection Regulation, machine learning techniques to forecast posterior click distribution based on the sequences of an identified user's actions are restricted in European countries. To overcome this barrier, we introduce a contextual behavior concept for the advertising network environment and propose a new hybrid model, which we call the Long Short Term Memory-Hawkes model by combining a stochastic-based generative model and a machine learning-based predictive model. Also, to meet the computational efficiency for the heavy demand in mobile advertisement market, we define gradient exponent

SUBMITTER: Hwang S 

PROVIDER: S-EPMC7274406 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature