Next app prediction based on graph neural networks and self-attention enhancement

Next mobile app prediction aims to recommend the apps that users will most likely to use next based on their historical usage behavior. It is critical for optimizing app preloading strategies and personalized recommendations, enhancing the user experience on mobile devices. However, it faces fundamental challenges such as interactions sparsity, rapid expansion of the app ecosystem and long-term interest neglect. Besides, user preference changes over time and frequent application updates are also ignored in existing models. To overcome the limitations of existing methods in next-app prediction, particularly in personalized feature extraction and temporal dynamics modeling, we propose a temporal-personalized next-app prediction framework, which employs multi-perspective graph representation learning with self-attention mechanisms to enhance user and app embeddings. It can effectively capture both long-term and short-term evolving user interests in app usage, enhancing dynamic temporal features of users and apps. Moreover, it can integrate global interactions into graph representation learning by multi-perspective feature aggregations. With a context-aware attention fusion mechanism applied, we effectively integrate temporal and personalized features to user and app representations. The comprehensive embeddings are obtained to next-app prediction, which significantly improve the accuracy of next app prediction. Experimental results on real datasets demonstrate that our model outperforms other baselines.