根据理想的偏好调整推算法使用户处于更好的状态
Poruz Khambatta1, Shwetha Mariadassou2, Joshua Morris2
1Anderson School of Management, University of California, Los Angeles, Los Angeles, CA, 90095, USA. poruz@ucla.edu.
Scientific reports
|June 8, 2023
概括
推算法可以帮助用户实现理想的偏好,而不仅仅是实际的偏好. 根据个人理想定制建议对用户和公司都有好处,提高了用户的满意度和参与度.
科学领域:
- 行为经济学是一种行为经济学.
- 人与计算机的互动.
- 计算社会科学 计算社会科学
背景情况:
- 个人经常经历实际和理想偏好之间的冲突,阻碍目标实现.
- 当前的推算法,通过优先考虑参与,可能会加剧这种冲突.
- 个性化推可以优化,以与用户的愿望目标保持一致.
研究的目的:
- 调查定制推算法以符合理想偏好,而不是实际偏好,是否有利于用户和公司.
- 评估偏好一致的推系统对用户满意度和业务指标的影响.
主要方法:
- 开发了针对实际或理想偏好的个性化推系统.
- 进行了一项大规模的,预先注册的实验,共有6,488名参与者.
- 测量用户结果,包括满意度,感知价值和支付意愿.
主要成果:
- 基于理想偏好的推导致点击次数减少,但增加了用户报告的幸福感和时间满意度.
- 针对理想的偏好增强了用户的支付意愿和对公司的信任.
- 用户更有可能继续使用满足他们理想偏好的服务.
结论:
- 将推算法从最大化参与到培养理想偏好提供了显著的优势.
- 将数字推动与用户的愿望相协调,可以为消费者和提供商创造更有益的体验.
- 未来的推系统应该旨在理解和支持用户的长期目标和理想.
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