通过基于位置的社交网络实现个性化的智能旅游
Yuqi Shen1, Yuhan Wu2, Jingbo Song3
1College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China.
PeerJ. Computer science
|December 9, 2024
概括
本研究引入了基于知识的深度学习方法,用于使用基于位置的社交网络 (LBSNs) 进行个性化智能旅游建议. 它解决了数据挑战,以提高旅行计划和了解移动模式.
科学领域:
- 计算机科学 计算机科学
- 信息科学 信息科学 信息科学
- 旅游学研究 旅游学研究
背景情况:
- 由于移动互联网,物联网和通信技术的进步,旅游业正在迅速发展,这导致了智能旅游的兴起.
- 基于位置的社交网络 (LBSNs) 为智能旅游提供了丰富的数据,但在多源信息建模和数据稀疏性方面面临挑战.
- 个性化旅行计划和建议是智能旅游的关键特征,需要有效的数据分析技术.
研究的目的:
- 通过利用LBSN数据的深度学习,为智能旅游提出一种基于知识的个性化推方法.
- 解决旅游应用LBSN数据固有的多源信息建模和数据稀疏性的复杂性.
- 通过探索个性化的旅行行为和了解人类移动模式,增强用户的旅行计划和建议.
主要方法:
- 利用表示学习技术在LBSNs中建模上下文信息 (时间,空间,语义).
- 采用对比式学习技术进行数据增强,以探索用户行为并减轻数据稀疏性.
- 进行了以旅行建议为重点的案例研究,以验证拟议的方法.
主要成果:
- 提出的基于知识的方法有效地模拟了来自LBSN的上下文信息,以提供个性化的建议.
- 相反的学习策略成功地减轻了数据稀疏性,并揭示了用户个性化的旅行行为.
- 案例研究表明了旅行推方法的实际有效性.
结论:
- 开发的深度学习方法为使用LBSN数据的个性化智能旅游建议提供了强大的解决方案.
- 该方法有效地处理复杂的数据挑战,为更智能的旅游服务铺平道路.
- 通过分析情境数据和旅游者偏好,获得了对人类流动模式的洞察.
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