智能旅游推系统中的多目标上下文盗版
1L2IS Laboratory, FST, Cadi Ayyad University, Marrakesh, Morocco. sara.qassimi@uca.ac.ma.
Scientific reports
|April 21, 2025
概括
这项研究引入了一种新的多目标上下文多武装强盗 (MOC-MAB) 系统,以改进个性化的旅行建议. MOC-MAB方法平衡了相关性和公平性,增强了智能旅游体验.
科学领域:
- 人工智能的人工智能
- 计算机科学 计算机科学
- 旅游管理 旅游管理
背景情况:
- 推系统对于个性化智能旅游至关重要,但上下文意识的系统面临着实时适应和多目标平衡的挑战.
- 信息过载使旅游决策变得复杂,需要先进的推策略.
研究的目的:
- 为智能旅游开发一个新的多目标的环境多武装强盗 (MOC-MAB) 推系统.
- 解决现有系统在实时适应和平衡相关性和公平性等多个目标方面的局限性.
主要方法:
- 拟议的系统将上下文盗窃算法与多目标优化集成在一起.
- 它从用户反中动态学习,以根据相关性和公平性优化建议.
- 在模拟和现实数据集 (TripAdvisor) 上进行了实验.
主要成果:
- 与基线方法相比,MOC-MAB方法显示出更高的性能.
- 关键绩效指标包括更高的累积奖励,更好的点击率和尽量减少遗憾.
- 该系统在智能旅游场景中显示出实际适用性,例如在马拉喀什.
结论:
- MOC-MAB系统有效地提高了智能旅游的个性化建议.
- 它提供了一个强大的解决方案来平衡多个目标,改善用户体验和决策.
- 该研究强调了先进推系统在智能城市旅游发展中的潜力.
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