  This article presents Appformer, a novel mobile application prediction
framework inspired by the efficiency of Transformer-like architectures in
processing sequential data through self-attention mechanisms. Combining a
Multi-Modal Data Progressive Fusion Module with a sophisticated Feature
Extraction Module, Appformer leverages the synergies of multi-modal data fusion
and data mining techniques while maintaining user privacy. The framework
employs Points of Interest (POIs) associated with base stations, optimizing
them through comprehensive comparative experiments to identify the most
effective clustering method. These refined inputs are seamlessly integrated
into the initial phases of cross-modal data fusion, where temporal units are
encoded via word embeddings and subsequently merged in later stages. The
Feature Extraction Module, employing Transformer-like architectures specialized
for time series analysis, adeptly distils comprehensive features. It
meticulously fine-tunes the outputs from the fusion module, facilitating the
extraction of high-calibre, multi-modal features, thus guaranteeing a robust
and efficient extraction process. Extensive experimental validation confirms
Appformer's effectiveness, attaining state-of-the-art (SOTA) metrics in mobile
app usage prediction, thereby signifying a notable progression in this field.
