Near Real Time AI Personalization for Notifications at LinkedIn

Abstract

Notifications at LinkedIn are very crucial for our members to stay informed about their network, discover professionally relevant content, conversations and courses, as well as identify potential career opportunities. For the Notifications AI team, our mission is to use AI to notify the right members, about the right content, at the right time and frequency through the right channel (push, in app or email) to maximize member value. In this talk we will give an overview of the AI systems and models behind these decisions. We will present the candidate generation systems as well as the final relevance layer, built on top of the Air Traffic Controller (ATC), to enable volume optimization, notification channel (badge, push or email) selection and state aware message spacing based delivery time optimization. We describe how we formulated a multi-objective optimization problem, considering multiple objectives that capture member and business impact on the entire ecosystem. This problem considers three types of utilities: whether a member visits, their engagement on the notifications, and their overall engagement on LinkedIn. We will explain the final decision function, derived from the multi-objective optimization formulation, and show that it can be applied in a streaming fashion. The final decision function is tuned online, through a hyperparameter tuning solution developed at Linkedin which allows us to fine tune tradeoffs in the multi-objective optimization approach. We will conclude with a discussion on some of the wins this has enabled, managing most of the notifications sent to our 774million+ members.