STAP: A Shuffle-Tokenized App Predictor with Ultra Long Context for Vocabulary-Free Mobile App Prediction
arXiv:2605.29863v1 Announce Type: new Abstract: Predicting the next mobile application a user will launch is essential for intelligent device resource management and proactive assistance. Existing models rely on fixed app vocabularies, which prevents them from generalizing across different app ecosystems. Many also depend on user-specific knowledge, which complicates deployment in cold start scenarios. We propose STAP, a Transformer-based model that eliminates the need for a fixed vocabulary. STAP replaces true app identities with randomly reassigned virtual indices via a shuffle mechanism, an
The proliferation of diverse mobile app ecosystems and increasing demand for intelligent, personalized user experiences are driving innovation in predictive AI.
This development represents a significant step towards more flexible and robust AI models that can operate across various platforms and address cold start problems, improving user experience and device efficiency.
Mobile app prediction models can now be deployed without reliance on fixed, ecosystem-specific vocabularies or extensive pre-existing user data, enabling wider applicability and faster integration.
- · Mobile device manufacturers
- · AI developers
- · App developers
- · Users
- · Legacy app prediction models
- · Proprietary app ecosystems dependent on fixed vocabularies
More efficient resource management on mobile devices and more seamless user experiences through proactive app assistance.
Accelerated adoption of generalized AI models in other context-dependent predictive tasks beyond mobile apps, due to this architectural innovation.
Potential for new forms of personalized, device-agnostic intelligent assistants that learn and adapt across diverse digital environments.
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Read at arXiv cs.LG