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Orthogonal Trajectories

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Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

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

Dongdong Feng¹, Siyao Li¹, Yong Xiang¹

¹China Telecom Research Institute, Guangzhou, Guangdong Province, China.

Peerj. Computer Science

|March 10, 2025

Summary

This study introduces an IoT-based algorithm for predicting user equipment (UE) cell transitions. The model achieves high performance with a recall of 0.5766, improving sequential recommendation systems.

Keywords:

Attention mechanism Sequential recommendation Trajectory prediction User embedding

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Area of Science:

Computer Science
Artificial Intelligence
Telecommunications

Background:

Sequential recommendation systems are crucial for personalized services.
Predicting user equipment (UE) movement in cellular networks is vital for efficient resource allocation and service delivery.

Purpose of the Study:

To develop an algorithm using Internet of Things (IoT) data to predict the next cell a user equipment (UE) will reach.
To enhance the precision and dependability of next-cell prediction models.

Main Methods:

Exploited UE and cell embeddings combined with visit time interval information.
Utilized sliding window sampling for processing trajectory data.
Applied an attention mechanism, omitting query matrix and attention mask, to extract key information and reduce parameters for faster training.
Incorporated positive and negative sampling with cross-entropy loss in the prediction layer.
Considered six adjacent cells as candidates for next-cell prediction.

Main Results:

Achieved a recall of 0.5766 in predicting the next destination cell.
Demonstrated optimal results and high performance through extensive empirical study.

Conclusions:

The proposed IoT-based algorithm effectively predicts next-cell transitions for user equipment.
The model offers improved precision and dependability, outperforming existing methods in sequential recommendation contexts.