MBSCL-Net:为下一个兴趣点的建议提供多分支光谱网络和对比学习

Sucheng Wang¹, Jinlai Zhang², Tao Zeng¹

¹Elite Engineering School, Changsha University of Science and Technology, Changsha 410114, China.

Sensors (Basel, Switzerland)

|September 27, 2025

概括

本研究介绍了MBSCL-Net,这是一种用于下一个感兴趣点 (POI) 建议的新方法. 它通过有效整合多模式数据和建模个性化的用户偏好来提高预测准确性,优于现有的方法.

科学领域:

* 计算社会科学 * 计算社会科学
* 数据科学数据科学
* 人工智能 * 人工智能

背景情况:

*下一个感兴趣点 (POI) 建议对于城市规划和交通管理至关重要.
* 现有的方法在多模式数据集成和捕捉偶然的用户行为方面扎.
* 用户偏好建模中的个性化仍然是当前推系统面临的挑战.

研究的目的:

* 开发一个先进的下一个POI推系统,解决现有方法的局限性.
* 通过多部门的方法有效地整合位置,时间和类别信息.
* 增强个性化用户偏好和偶然行为的建模.

主要方法:

* 提出具有对比学习的多分支光谱网络 (MBSCL-Net).
* 使用多头注意力机制来提取和合并位置,时间和类别数据的不同特征.
* 应用富里埃转换来将时间域检查数据转换为频域,增强周期信号并抑制噪声.
* 结合对比性学习损失来区分用户行为模式,并完善个性化偏好建模.

主要成果:

*在下一个POI推中,MBSCL-Net与最先进的 (SOTA) 方法相比显示出更高的性能.
*有效整合多式联运特征可以提高预测准确度.
*对用户行为进行了改进的建模,包括定期和偶然的模式,有助于提供更好的建议.

结论:

* MBSCL-Net通过有效处理多模式数据和个性化,为下一个POI建议提供了强大的解决方案.
* 建议的光谱转换和对比学习技术显著提高了系统捕获复杂用户移动模式的能力.
* 通过准确的移动性预测,这些发现对优化城市规划,交通管理和商业战略有意义.

关键词:

富里叶变换是什么意思富里叶变换注意力机制注意力机制相反的学习学习学习.下一个POI推.

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