A user-embedded temporal attention neural network for IoT trajectories prediction.

ABSTRACT: Over the past two decades, sequential recommendation systems have garnered significant research interest, driven by their potential applications in personalized product recommendations. In this article, we seek to explicitly model an algorithm based on Internet of Things (IoT) data to predict the next cell reached by the user equipment (UE). This algorithm exploits UE embedding and cell embedding combining the visit time interval information, and uses sliding window sampling to process more UE trajectory data. Furthermore, we use the attention mechanism, removed the query matrix operation and the attention mask, to obtain key information in data and reduce the number of parameters to speed up training. In the prediction layer, combining the positive and negative sampling and computing cros

SUBMITTER: Feng D 

PROVIDER: S-EPMC11888941 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature