A Point Process Model for Optimizing Repeated Personalized Action Delivery to Users

TL;DR

This paper introduces a formal framework for causal inference in user-advertiser interactions, extending temporal point processes and proposing neural point processes as practical solutions for personalized action delivery optimization.

Contribution

It develops a formalism for causal inference in user-advertiser interactions and extends point process models with neural approaches for practical application.

Findings

Formalism for causal inference in user-advertiser interactions

Extension of temporal marked point processes

Neural point processes as practical solutions

Abstract

This paper provides a formalism for an important class of causal inference problems inspired by user-advertiser interaction in online advertiser. Then this formalism is specialized to an extension of temporal marked point processes and the neural point processes are suggested as practical solutions to some interesting special cases.