# LiMAML: Personalization of Deep Recommender Models via Meta Learning

arXiv: https://arxiv.org/abs/2403.00803 · HTML: https://mlsanity.com/paper/2403.00803/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 37 references that are in the map.
- First posted on arXiv 23 Feb 2024.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour](https://mlsanity.com/paper/1706.02677/) (2017)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [Meta-Learning with Implicit Gradients](https://mlsanity.com/paper/1909.04630/) (2019)
- [Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML](https://mlsanity.com/paper/1909.09157/) (2019)
- [Deep Learning Recommendation Model for Personalization and Recommendation Systems](https://mlsanity.com/paper/1906.00091/) (2019)
- [DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems](https://mlsanity.com/paper/2008.13535/) (2020)
- [Product-based Neural Networks for User Response Prediction](https://mlsanity.com/paper/1611.00144/) (2016)
- [MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation](https://mlsanity.com/paper/1908.00413/) (2019)
- [Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings](https://mlsanity.com/paper/1904.11547/) (2019)
- [Meta-Learning Online Adaptation of Language Models](https://mlsanity.com/paper/2305.15076/) (2023)
- [Sequential Scenario-Specific Meta Learner for Online Recommendation](https://mlsanity.com/paper/1906.00391/) (2019)
- [Personalized Adaptive Meta Learning for Cold-start User Preference Prediction](https://mlsanity.com/paper/2012.11842/) (2020)
- [MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection](https://mlsanity.com/paper/2001.10378/) (2020)
- [Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate Prediction](https://mlsanity.com/paper/2105.08909/) (2021)
- [Meta-Learning for Online Update of Recommender Systems](https://mlsanity.com/paper/2203.10354/) (2022)
- [Learning an Adaptive Meta Model-Generator for Incrementally Updating Recommender Systems](https://mlsanity.com/paper/2111.04282/) (2021)
- [Deep Meta-learning in Recommendation Systems: A Survey](https://mlsanity.com/paper/2206.04415/) (2022)
- [G-Meta: Distributed Meta Learning in GPU Clusters for Large-Scale Recommender Systems](https://mlsanity.com/paper/2401.04338/) (2024)
- [A Dynamic Meta-Learning Model for Time-Sensitive Cold-Start Recommendations](https://mlsanity.com/paper/2204.00970/) (2022)

## Built on by (most cited first, 2 of 2)

- [Learning to Imitate with Less: Efficient Individual Behavior Modeling in Chess](https://mlsanity.com/paper/2507.21488/) (2025)
- [Towards Unveiling Predictive Uncertainty Vulnerabilities in the Context of the Right to Be Forgotten](https://mlsanity.com/paper/2508.07458/) (2025)

## Abstract (arXiv)

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.