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Despite the rapid advancement of mobile applications, predicting app usageremains a formidable challenge due to intricate user behaviours andever-evolving contexts. To address these issues, this paper introduces theMobile App Prediction Leveraging Large Language Model Embeddings (MAPLE) model.This innovative approach utilizes Large Language Models (LLMs) to predict appusage accurately. Rigorous testing on two public datasets highlights MAPLE'scapability to decipher intricate patterns and comprehend user contexts. Theserobust results confirm MAPLE's versatility and resilience across variousscenarios. While its primary design caters to app prediction, the outcomes alsoemphasize the broader applicability of LLMs in different domains. Through thisresearch, we emphasize the potential of LLMs in app usage prediction andsuggest their transformative capacity in modelling human behaviours acrossdiverse fields.

MAPLE: Mobile App Prediction Leveraging Large Language model Embeddings