准确的POI推随机组,改进了图形神经网络和多谈判模型
Xiaoyu Song1, Zhizhong Liu2, Lingqiang Meng1
1School of Computer and Control Engineering, Yantai University, Yantai, 264005, China.
Scientific reports
|March 3, 2025
概括
这项研究引入了一个新的模型,用于为随机组准确的兴趣点 (POI) 建议,考虑个人的个性. 该模型通过直接选择最佳的POI来增强用户体验,改善组决策.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 人与计算机的交互
背景情况:
- 集团活动正在增加,推动对集团关注点 (POI) 建议的需求.
- 对于固定群体的现有方法是先进的,但对随机群体的个性意识建议仍未得到充分探索.
- 现有的系统提供POI列表,导致小组选择和用户体验不佳.
研究的目的:
- 为随机组解决人格意识的POI建议的差距.
- 通过直接推最佳的POI而不是列表来改善用户体验.
- 提出一种新型模型,准确的POI推随机组与改进的图形神经网络和多谈判模型 (APRRGM).
主要方法:
- APRRGM使用成员个性和POI交互数据生成组特征.
- 它使用改进的图形神经网络 (GNN) 学习POI特征,结合成员个性.
- 一个多谈判模型从基于组特征和POI特征的推序列中确定最佳的POI.
主要成果:
- 在Yelp,Gowalla和Foursquare数据集上进行了广泛的实验.
- 与现有的基线模型相比,APRRGM表现出优越的性能.
- 该模型有效地将人格特征整合到随机组的推过程中.
结论:
- APRRGM为随机组提供了一个准确和改进的POI推方法.
- 整合个性意识特征和多谈判策略提高了推质量和用户满意度.
- 这项工作推动了个性化团体推系统领域的发展.
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