MISApp: Multi-Hop Intent-Aware Session Graph Learning for Next App Prediction

TL;DR

MISApp is a novel multi-hop session graph learning framework that improves next app prediction accuracy in mobile usage scenarios, especially under cold-start conditions, by capturing higher-order dependencies and intent evolution.

Contribution

It introduces a profile-free, multi-hop session graph approach that models structural dependencies and intent dynamics for next app prediction, addressing limitations of existing sequential models.

Findings

Outperforms baseline methods in accuracy on real-world datasets.

Effective in cold-start scenarios with sparse user profiles.

Provides interpretable attention weights aligned with structural relevance.

Abstract

Predicting the next mobile app a user will launch is essential for proactive mobile services. Yet accurate prediction remains challenging in real-world settings, where user intent can shift rapidly within short sessions and user-specific historical profiles are often sparse or unavailable, especially under cold-start conditions. Existing approaches mainly model app usage as sequential behavior or local session transitions, limiting their ability to capture higher-order structural dependencies and evolving session intent. To address this issue, we propose MISApp, a profile-free framework for next app prediction based on multi-hop session graph learning. MISApp constructs multi-hop session graphs to capture transition dependencies at different structural ranges, learns session representations through lightweight graph propagation, incorporates temporal and spatial context to characterize session conditions, and captures intent evolution from recent interactions. Experiments on two real-world app usage datasets show that MISApp consistently outperforms competitive baselines under both standard and cold-start settings, while maintaining a favorable balance between predictive accuracy and practical efficiency. Further analyses show that the learned hop-level attention weights align well with structural relevance, offering interpretable evidence for the effectiveness of the proposed multi-hop modeling strategy.