Despite the rapid advancement of mobile applications, predicting app usage
remains a formidable challenge due to intricate user behaviours and
ever-evolving contexts. To address these issues, this paper introduces the
Mobile App Prediction Leveraging Large Language Model Embeddings (MAPLE) model.
This innovative approach utilizes Large Language Models (LLMs) to predict app
usage accurately. Rigorous testing on two public datasets highlights MAPLE's
capability to decipher intricate patterns and comprehend user contexts. These
robust results confirm MAPLE's versatility and resilience across various
scenarios. While its primary design caters to app prediction, the outcomes also
emphasize the broader applicability of LLMs in different domains. Through this
research, we emphasize the potential of LLMs in app usage prediction and
suggest their transformative capacity in modelling human behaviours across
diverse fields.

本研究提出了基于大型语言模型嵌入的移动应用预测模型（MAPLE），以应对移动应用预测中复杂的用户行为和不断演化的环境。通过对两个公共数据集的严格测试，结果表明 MAPLE 能够准确解读复杂模式并理解用户背景。这些强大的结果确认了 MAPLE 在不同场景中的多功能性和适应性。虽然其主要设计面向应用预测，但研究结果也强调了大型语言模型在不同领域中的广泛适用性。通过这项研究，我们强调了大型语言模型在应用使用预测中的潜力，并建议它们在模拟人类行为方面具有改变性的能力。