# TGT: A Temporal Gating Transformer for Smartphone App Usage Prediction

arXiv: https://arxiv.org/abs/2502.16957 · HTML: https://mlsanity.com/paper/2502.16957/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 53 references that are in the map.
- First posted on arXiv 24 Feb 2025.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [Are Transformers Effective for Time Series Forecasting?](https://mlsanity.com/paper/2205.13504/) (2022)
- [FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting](https://mlsanity.com/paper/2201.12740/) (2022)
- [TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis](https://mlsanity.com/paper/2210.02186/) (2022)
- [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015)
- [Frequency-domain MLPs are More Effective Learners in Time Series Forecasting](https://mlsanity.com/paper/2311.06184/) (2023)
- [SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters](https://mlsanity.com/paper/2405.00946/) (2024)
- [CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patterns](https://mlsanity.com/paper/2409.18479/) (2024)
- [ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling](https://mlsanity.com/paper/2402.10635/) (2024)
- [Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment](https://mlsanity.com/paper/1510.04026/) (2015)
- [Smartphone App Usage Prediction Using Points of Interest](https://mlsanity.com/paper/1711.09337/) (2017)
- [Context-Aware Target Apps Selection and Recommendation for Enhancing Personal Mobile Assistants](https://mlsanity.com/paper/2101.03394/) (2021)
- [CoSEM: Contextual and Semantic Embedding for App Usage Prediction](https://mlsanity.com/paper/2108.11561/) (2021)
- [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)
- [Building Energy Consumption Models Based On Smartphone User's Usage Patterns](https://mlsanity.com/paper/2012.10246/) (2020)

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

- [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)

Accurately predicting smartphone app usage is challenging due to the sparsity and irregularity of user behavior, especially under cold-start and low-activity conditions. Existing approaches mostly rely on static or attention-only architectures, which struggle to model fine-grained temporal dynamics. We propose TGT, a Transformer framework equipped with a temporal gating module that conditions hidden representations on the hour-of-day. Unlike conventional time embeddings, temporal gating adaptively rescales feature dimensions in a time-aware manner, working orthogonally to self-attention and strengthening temporal sensitivity. TGT further incorporates a context-aware encoder that integrates session sequences and user profiles into a unified representation. Experiments on two real-world datasets, Tsinghua App Usage and LSApp, demonstrate that TGT significantly outperforms 15 competitive baselines, achieving notable gains in HR@1 and maintaining robustness under cold-start scenarios. Beyond accuracy, analysis of gating vectors uncovers interpretable daily usage rhythms, showing that TGT learns human-consistent patterns of app behavior. These results establish TGT as both a powerful and interpretable framework for time-aware app usage prediction.