# Smartphone App Usage Prediction Using Points of Interest

arXiv: https://arxiv.org/abs/1711.09337 · HTML: https://mlsanity.com/paper/1711.09337/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2017.

## Built on by (most cited first, 15 of 15)

- [CoSEM: Contextual and Semantic Embedding for App Usage Prediction](https://mlsanity.com/paper/2108.11561/) (2021)
- [DisenHCN: Disentangled Hypergraph Convolutional Networks for Spatiotemporal Activity Prediction](https://mlsanity.com/paper/2208.06794/) (2022)
- [Learning-augmented Online Algorithm for Two-level Ski-rental Problem](https://mlsanity.com/paper/2402.06715/) (2024)
- [Modeling Spatial Trajectories using Coarse-Grained Smartphone Logs](https://mlsanity.com/paper/2208.13775/) (2022)
- [Modeling Time-Series and Spatial Data for Recommendations and Other Applications](https://mlsanity.com/paper/2212.13259/) (2022)
- [Appformer: A Novel Framework for Mobile App Usage Prediction Leveraging Progressive Multi-Modal Data Fusion and Feature Extraction](https://mlsanity.com/paper/2407.19414/) (2024)
- [TGT: A Temporal Gating Transformer for Smartphone App Usage Prediction](https://mlsanity.com/paper/2502.16957/) (2025)
- [Federated Privacy-preserving Collaborative Filtering for On-Device Next App Prediction](https://mlsanity.com/paper/2303.04744/) (2023)
- [MISApp: Multi-Hop Intent-Aware Session Graph Learning for Next App Prediction](https://mlsanity.com/paper/2603.21653/) (2026)
- [On-device modeling of user's social context and familiar places from smartphone-embedded sensor data](https://mlsanity.com/paper/2205.08790/) (2022)
- [Characterizing Human Actions in the Digital Platform by Temporal Context](https://mlsanity.com/paper/2206.09535/) (2022)
- [OpenCarbon: A Contrastive Learning-based Cross-Modality Neural Approach for High-Resolution Carbon Emission Prediction Using Open Data](https://mlsanity.com/paper/2506.03224/) (2025)
- [TextOnly: A Unified Function Portal for Text-Related Functions on Smartphones](https://mlsanity.com/paper/2508.16926/) (2025)
- [U-MASK: User-adaptive Spatio-Temporal Masking for Personalized Mobile AI Applications](https://mlsanity.com/paper/2601.06867/) (2026)
- [STAP: A Shuffle-Tokenized App Predictor with Ultra Long Context for Vocabulary-Free Mobile App Prediction](https://mlsanity.com/paper/2605.29863/) (2026)

## Abstract (arXiv)

In this paper we present the first population-level, city-scale analysis of application usage on smartphones. Using deep packet inspection at the network operator level, we obtained a geo-tagged dataset with more than 6 million unique devices that launched more than 10,000 unique applications across the city of Shanghai over one week. We develop a technique that leverages transfer learning to predict which applications are most popular and estimate the whole usage distribution based on the Point of Interest (POI) information of that particular location. We demonstrate that our technique has an 83.0% hitrate in successfully identifying the top five popular applications, and a 0.15 RMSE when estimating usage with just 10% sampled sparse data. It outperforms by about 25.7% over the existing state-of-the-art approaches. Our findings pave the way for predicting which apps are relevant to a user given their current location, and which applications are popular where. The implications of our findings are broad: it enables a range of systems to benefit from such timely predictions, including operating systems, network operators, appstores, advertisers, and service providers.