科斯塔:为下一个POI建议提供对比的空间和时间调整框架.
Yu Lei1, Limin Shen1, Zhu Sun2
1School of Computer Science and Engineering, Yanshan University, Qinhuangdao, 066004, Hebei, People's Republic of China; Key Lab for Sofware Engineering of Heibei Province, Qinhuangdao, 066004, Hebei, People's Republic of China.
概括
本研究确定了下一个兴趣点 (POI) 建议中的空间和时间偏差. 一个新的框架,Costa,有效地减少了这些偏见,改善了用户体验,而不牺牲推准确性.
科学领域:
- 数据科学数据科学数据科学
- 人工智能的人工智能
- 人与计算机的交互
背景情况:
- 下一个感兴趣点 (POI) 推系统分析用户移动轨迹.
- 现有的方法经常引入空间和时间偏差,使建议与用户偏好不一致.
- 这些偏见会对基于位置的服务的用户体验产生负面影响.
研究的目的:
- 在下一个POI建议中揭示和分析空间和时间偏差的有害影响.
- 提出一个新的框架,COSTA,以减轻这些被发现的偏见.
- 引入用于量化空间和时间偏差严重性的新指标.
主要方法:
- 开发了对比空间和时间偏移 (COSTA) 框架.
- 利用了用户和位置侧的时空信号编码器.
- 采用对比学习来调整用户和POI表示.
- 引入了折扣空间累积收益 (DSCG) 和折扣时间累积收益 (DTCG) 的指标.
主要成果:
- 在下一个POI建议中,COSTA有效地减轻了空间和时间偏差.
- 拟议的脱度指标 (DSCG,DTCG) 量化了偏差严重程度.
- 在数据处理方面,COSTA的表现优于现实世界数据集的最新方法.
- 在降低偏差的同时保持推准确度.
结论:
- 空间和时间偏差是下一个POI建议中的重要问题.
- 哥斯达提供了一个有效的解决方案,用于推系统的退化.
- 新的指标为评估POI建议中的偏见提供了有价值的工具.
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