山西大学学报（自然科学版）2026，Vol.49Issue(4)：525-534,10.DOI:10.13451/j.sxu.ns.2025128
基于大语言模型语义增强的移动应用使用预测方法
Semantic-enhanced Application Usage Prediction via Large Language Models
摘要
Abstract
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.关键词
分布外泛化/嵌入模型/Mamba-2Key words
out-of-distribution generalization/embedding model/Mamba-2分类
信息技术与安全科学引用本文复制引用
康梅英,尚若冰,陈文亮..基于大语言模型语义增强的移动应用使用预测方法[J].山西大学学报（自然科学版）,2026,49(4):525-534,10.基金项目
国家自然科学基金(62036004) （62036004）
江苏高校优势学科建设工程资助项目 （）
