LiMAML: Personalization of Deep Recommender Models via Meta Learning
Ruofan Wang Prakruthi Prabhakar Gaurav Srivastava Tianqi Wang Zeinab S. Jalali Varun Bharill Yunbo Ouyang Aastha Nigam Divya Venugopalan Aman Gupta Fedor Borisyuk Sathiya Keerthi Ajith Muralidharan
In the realm of recommender systems, the ubiquitous adoption of deep neural
networks has emerged as a dominant paradigm for modeling diverse business
objectives. As user bases continue to expand, the necessity of personalization
and frequent model updates have assumed paramount significance to ensure the
delivery of relevant and refreshed experiences to a diverse array of members.
In this work, we introduce an innovative meta-learning solution tailored to the
personalization of models for individual members and other entities, coupled
with the frequent updates based on the latest user interaction signals.
Specifically, we leverage the Model-Agnostic Meta Learning (MAML) algorithm to
adapt per-task sub-networks using recent user interaction data. Given the near
infeasibility of productionizing original MAML-based models in online
recommendation systems, we propose an efficient strategy to operationalize
meta-learned sub-networks in production, which involves transforming them into
fixed-sized vectors, termed meta embeddings, thereby enabling the seamless
deployment of models with hundreds of billions of parameters for online
serving. Through extensive experimentation on production data drawn from
various applications at LinkedIn, we demonstrate that the proposed solution
consistently outperforms the baseline models of those applications, including
strong baselines such as using wide-and-deep ID based personalization approach.
Our approach has enabled the deployment of a range of highly personalized AI
models across diverse LinkedIn applications, leading to substantial
improvements in business metrics as well as refreshed experience for our
members.