基于大语言模型语义增强的移动应用使用预测方法OA

Application usage prediction is a crucial technique for optimizing smartphone systems.This study proposes a semantic-en-hanced prediction method based on large language models to improve the understanding of user intent and generalization to out-of-distribution applications.The method used a large language model to automatically generate functional descriptions of applications.These descriptions were encoded into high-dimensional semantic vectors through an embedding model and used as semantic repre-sentations of historical usage sequences.These representations were then fed into the prediction model.To better capture temporal dynamics and semantic dependencies in user behavior,a prediction model was designed by integrating locally activated attention mechanisms with a Mamba-2 module.Experimental results showed that on a real-world application usage dataset,the proposed method increased the Top-3 accuracy from 76.41%to 77.00%on in-distribution data and from 66.06%to 71.57%on out-of-distribu-tion data,significantly improving the model's generalization to unseen applications.