  In recent years, predicting mobile app usage has become increasingly
important for areas like app recommendation, user behaviour analysis, and
mobile resource management. Existing models, however, struggle with the
heterogeneous nature of contextual data and the user cold start problem. This
study introduces a novel prediction model, Mobile App Prediction Leveraging
Large Language Model Embeddings (MAPLE), which employs Large Language Models
(LLMs) and installed app similarity to overcome these challenges. MAPLE
utilises the power of LLMs to process contextual data and discern intricate
relationships within it effectively. Additionally, we explore the use of
installed app similarity to address the cold start problem, facilitating the
modelling of user preferences and habits, even for new users with limited
historical data. In essence, our research presents MAPLE as a novel, potent,
and practical approach to app usage prediction, making significant strides in
resolving issues faced by existing models. MAPLE stands out as a comprehensive
and effective solution, setting a new benchmark for more precise and
personalised app usage predictions. In tests on two real-world datasets, MAPLE
surpasses contemporary models in both standard and cold start scenarios. These
outcomes validate MAPLE's capacity for precise app usage predictions and its
resilience against the cold start problem. This enhanced performance stems from
the model's proficiency in capturing complex temporal patterns and leveraging
contextual information. As a result, MAPLE can potentially improve personalised
mobile app usage predictions and user experiences markedly.
