PCDe:为下一个POI推提供个性化对话式退款框架,不确定登记时间
Chen Li1, Guoyan Huang1, Zhu Sun2
1School of Computer Science and Engineering, Yanshan University, Qinhuangdao, 066000, China.
概括
新的研究解决了对感兴趣点 (POI) 建议的偏见,特别是在大型场馆内. 一个新的框架,个性化对话偏差 (PCDe),有效地减少了规模和人气偏差,以获得更公平,更准确的POI建议.
科学领域:
- 计算机科学 计算机科学
- 人与计算机的交互
- 数据科学数据科学数据科学
背景情况:
- 下一个兴趣点 (POI) 建议面临的挑战是,在集体POI (例如购物中心) 进行不确定的登记.
- 不确定的登记引入了规模偏差 (有利于集体而不是个人POI),并加剧了人气偏差 (有利于受欢迎而不是不受欢迎的POI).
- 这些偏见显著影响了POI推系统的公平性.
研究的目的:
- 在下一个POI建议中提出一个新的框架,以减轻规模和受欢迎程度的偏见.
- 通过解决个性化的用户偏好来提高POI推系统的公平性和准确性.
- 引入一个能解释用户行为在集体POI中的 debiasing 机制.
主要方法:
- 开发了一个个性化对话调整 (PCDe) 框架,利用对话技巧.
- 实现了一个使用个性化信息来减轻规模偏差的查询组件.
- 引入了一个奖励组件,用Jensen-Shannon分歧来解决受欢迎偏见的愚蠢奖励机制.
主要成果:
- PCDe框架有效地减轻了下一个POI建议中的规模和受欢迎程度偏差.
- 实验结果表明PCDe在最先进的方法上的优越性.
- 通过解决个性化的用户偏好和偏见,PCDe提高了推准确性.
结论:
- 个性化淘汰策略对于公平而准确的POI建议至关重要,尤其是在不确定的登记时.
- 交谈技巧提供了一种有希望的方法来捕捉动态用户偏好并减轻推偏见.
- 拟议的PCDe框架为提高POI推系统的公平性和性能提供了有效的解决方案.
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