Atten-Transformer: A Deep Learning Framework for User App Usage Prediction

• URL: http://arxiv.org/abs/2502.16957v1
• Date: Mon, 24 Feb 2025 08:31:51 GMT
• Title: Atten-Transformer: A Deep Learning Framework for User App Usage Prediction
• Authors: Longlong Li, Cunquan Qu, Guanghui Wang,
• Abstract summary: We introduce Atten-Transformer, a novel model that integrates temporal attention with a Transformer network to dynamically identify and leverage key app usage patterns.<n>Our approach employs a multi-dimensional feature representation, incorporating both feature encoding and temporal encoding to enhance predictive accuracy.
• Score: 6.081915850400204
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurately predicting smartphone app usage patterns is crucial for user experience optimization and targeted marketing. However, existing methods struggle to capture intricate dependencies in user behavior, particularly in sparse or complex usage scenarios. To address these challenges, we introduce Atten-Transformer, a novel model that integrates temporal attention with a Transformer network to dynamically identify and leverage key app usage patterns. Unlike conventional methods that primarily consider app order and duration, our approach employs a multi-dimensional feature representation, incorporating both feature encoding and temporal encoding to enhance predictive accuracy. The proposed attention mechanism effectively assigns importance to critical app usage moments, improving both model interpretability and generalization. Extensive experiments on multiple smartphone usage datasets, including LSapp and Tsinghua App Usage datasets, demonstrate that Atten-Transformer consistently outperforms state-of-the-art models across different data splits. Specifically, our model achieves a 45.24\% improvement in HR@1 on the Tsinghua dataset (Time-based Split) and a 18.25\% improvement in HR@1 on the LSapp dataset (Cold Start Split), showcasing its robustness across diverse app usage scenarios. These findings highlight the potential of integrating adaptive attention mechanisms in mobile usage forecasting, paving the way for enhanced user engagement and resource allocation.

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