# General-Purpose User Embeddings based on Mobile App Usage

arXiv: https://arxiv.org/abs/2005.13303 · HTML: https://mlsanity.com/paper/2005.13303/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2020.

## Built on by (most cited first, 11 of 11)

- [HET: Scaling out Huge Embedding Model Training via Cache-enabled Distributed Framework](https://mlsanity.com/paper/2112.07221/) (2021)
- [Scaling User Modeling: Large-scale Online User Representations for Ads Personalization in Meta](https://mlsanity.com/paper/2311.09544/) (2023)
- [One4all User Representation for Recommender Systems in E-commerce](https://mlsanity.com/paper/2106.00573/) (2021)
- [Async Learned User Embeddings for Ads Delivery Optimization](https://mlsanity.com/paper/2406.05898/) (2024)
- [Transformer-based unsupervised patient representation learning based on medical claims for risk stratification and analysis](https://mlsanity.com/paper/2106.12658/) (2021)
- [Transferable and Forecastable User Targeting Foundation Model](https://mlsanity.com/paper/2412.12468/) (2024)
- [Realizing Scaling Laws in Recommender Systems: A Foundation-Expert Paradigm for Hyperscale Model Deployment](https://mlsanity.com/paper/2508.02929/) (2025)
- [Learning Large-scale Universal User Representation with Sparse Mixture of Experts](https://mlsanity.com/paper/2207.04648/) (2022)
- [DashCLIP: Leveraging multimodal models for generating semantic embeddings for DoorDash](https://mlsanity.com/paper/2504.07110/) (2025)
- [USE: Dynamic User Modeling with Stateful Sequence Models](https://mlsanity.com/paper/2403.13344/) (2024)
- [ERCache: An Efficient and Reliable Caching Framework for Large-Scale User Representations in Meta's Ads System](https://mlsanity.com/paper/2410.06497/) (2024)

## Abstract (arXiv)

In this paper, we report our recent practice at Tencent for user modeling based on mobile app usage. User behaviors on mobile app usage, including retention, installation, and uninstallation, can be a good indicator for both long-term and short-term interests of users. For example, if a user installs Snapseed recently, she might have a growing interest in photographing. Such information is valuable for numerous downstream applications, including advertising, recommendations, etc. Traditionally, user modeling from mobile app usage heavily relies on handcrafted feature engineering, which requires onerous human work for different downstream applications, and could be sub-optimal without domain experts. However, automatic user modeling based on mobile app usage faces unique challenges, including (1) retention, installation, and uninstallation are heterogeneous but need to be modeled collectively, (2) user behaviors are distributed unevenly over time, and (3) many long-tailed apps suffer from serious sparsity. In this paper, we present a tailored AutoEncoder-coupled Transformer Network (AETN), by which we overcome these challenges and achieve the goals of reducing manual efforts and boosting performance. We have deployed the model at Tencent, and both online/offline experiments from multiple domains of downstream applications have demonstrated the effectiveness of the output user embeddings.