# CoSEM: Contextual and Semantic Embedding for App Usage Prediction

arXiv: https://arxiv.org/abs/2108.11561 · HTML: https://mlsanity.com/paper/2108.11561/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 26 references that are in the map.
- First posted on arXiv 26 Aug 2021.

## Builds on (most cited first, 3 of 3)

- [Smartphone App Usage Prediction Using Points of Interest](https://mlsanity.com/paper/1711.09337/) (2017)
- [Target Apps Selection: Towards a Unified Search Framework for Mobile Devices](https://mlsanity.com/paper/1805.02211/) (2018)
- [On the Feature Discovery for App Usage Prediction in Smartphones](https://mlsanity.com/paper/1309.7982/) (2013)

## Built on by (most cited first, 5 of 5)

- [MAPLE: Mobile App Prediction Leveraging Large Language Model Embeddings](https://mlsanity.com/paper/2309.08648/) (2023)
- [TGT: A Temporal Gating Transformer for Smartphone App Usage Prediction](https://mlsanity.com/paper/2502.16957/) (2025)
- [MobiGPT: A Foundation Model for Mobile Wireless Networks](https://mlsanity.com/paper/2509.18166/) (2025)
- [MISApp: Multi-Hop Intent-Aware Session Graph Learning for Next App Prediction](https://mlsanity.com/paper/2603.21653/) (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)

App usage prediction is important for smartphone system optimization to enhance user experience. Existing modeling approaches utilize historical app usage logs along with a wide range of semantic information to predict the app usage; however, they are only effective in certain scenarios and cannot be generalized across different situations. This paper address this problem by developing a model called Contextual and Semantic Embedding model for App Usage Prediction (CoSEM) for app usage prediction that leverages integration of 1) semantic information embedding and 2) contextual information embedding based on historical app usage of individuals. Extensive experiments show that the combination of semantic information and history app usage information enables our model to outperform the baselines on three real-world datasets, achieving an MRR score over 0.55,0.57,0.86 and Hit rate scores of more than 0.71, 0.75, and 0.95, respectively.