MAPLE: Mobile App Prediction Leveraging Large Language Model Embeddings

TL;DR

MAPLE is a novel mobile app prediction model that leverages large language model embeddings and app similarity to improve accuracy and address cold start issues, outperforming existing models on real-world datasets.

Contribution

The paper introduces MAPLE, a new prediction approach using LLMs and app similarity to enhance mobile app usage prediction and cold start problem handling.

Findings

MAPLE outperforms existing models on real-world datasets.

It effectively handles cold start scenarios.

It captures complex temporal and contextual patterns.

Abstract

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.