CAREER: Leveraging Recommendations for Self-Actualization

职业：利用自我实现的建议

• 批准号: 2045153
• 负责人: Bart Knijnenburg
• 金额: $ 54.61万
• 依托单位: Clemson University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2045153&HistoricalAwards=false
• 关键词: CAREER; Leveraging; Recommendations; Self; Actualization

The primary research goal of this project is to leverage recommendation technology to support users in developing, exploring, and understanding their preferences based on their long-term goals and ambitions. Recommender systems provide their users with recommendations based on their expressed preferences, but decision scientists have demonstrated that users' preferences are often constructed on the fly and thereby focused on their immediate desires rather than their longer-term goals. The recommendations, in turn, tend to reflect these short-term likes, ignoring users' ambitions and long-term goals. This proposal will take an important step towards escaping this vicious circle by developing and testing two novel user-adaptive interaction mechanisms that leverage recommendation algorithms to support unique new ways for users to develop, discover, and understand their own preferences. This will turn the users of these systems into confident consumers who are able to construct preferences that focus on their broader long-term objectives. The decision-support platform and the proposed interaction mechanisms will be made available to academics to support their research on recommender systems and decision-making.Two innovative interaction mechanisms can advance the field of recommender systems research as well as the field of decision science by extending the reach of personalized decision-support tools beyond "choice" towards preference construction: (1) Personalized preference profiles that use recommendation algorithms to visualize users' preferences and highlight surprising preference dynamics. These profiles will induce preference construction by helping users understand their preferences, reflect upon their long-term goals, and make better-informed decisions. (2) Preference-based communities that use recommendation algorithms to automatically create ad-hoc groups of users whose interests overlap enough to generate interesting discussion, yet not so much that they turn into epistemic bubbles. These communities seek to induce preference construction through discussion, thereby reinstating the social and intellectual benefits of active advice-giving practices. This project will integrate these interaction mechanisms into live systems, supporting education and outreach activities where students of all ages will use these systems to make decisions based on their preferences and life goals.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个项目的主要研究目标是利用推荐技术来支持用户根据他们的长期目标和抱负来开发、探索和理解他们的偏好。推荐系统根据用户表达的偏好向他们提供推荐，但决策科学家已经证明，用户的偏好通常是动态构建的，因此专注于他们的眼前需求，而不是他们的长期目标。反过来，这些推荐往往反映了这些短期的喜好，忽视了用户的雄心和长期目标。这项提议将通过开发和测试两种新颖的用户自适应交互机制，从而朝着摆脱这种恶性循环迈出重要的一步。这两种机制利用推荐算法来支持用户开发、发现和理解自己的偏好的独特新方法。这将把这些系统的用户转变为自信的消费者，他们能够构建专注于更广泛的长期目标的偏好。该决策支持平台和建议的交互机制将向学者提供支持，以支持他们对推荐系统和决策的研究。两种创新的交互机制可以通过将个性化决策支持工具的覆盖范围从“选择”扩展到偏好构建，从而推动推荐系统研究和决策科学领域的发展：(1)使用推荐算法可视化用户偏好并突出令人惊讶的偏好动态的个性化偏好简档。这些个人资料将通过帮助用户了解他们的偏好，反思他们的长期目标，并做出更明智的决定来诱导偏好构建。(2)基于偏好的社区，它使用推荐算法自动创建特定的用户组，这些用户的兴趣重叠程度足以引发有趣的讨论，但又不会太多，以至于变成认知泡沫。这些社区寻求通过讨论诱导偏好构建，从而恢复积极建议实践的社会和智力利益。这个项目将把这些互动机制整合到生活系统中，支持教育和外展活动，在这些活动中，所有年龄段的学生都将使用这些系统根据他们的喜好和人生目标做出决定。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Retiree Volunteerism: Automating "Word of Mouth" Communication

退休人员志愿服务：自动化“口碑”沟通

• 发表时间: 2023
• 期刊: CEUR workshop proceedings
• 作者: Black, J.;Michael, I;Roberts, D.;Stigall, B.;Knijnenburg, B.P.
• 通讯作者: Knijnenburg, B.P.

Designing a Recommender System to Recruit Older Adults for Research Studies

设计一个招募老年人进行研究的推荐系统

• 发表时间: 2023
• 期刊: CEUR workshop proceedings
• 作者: Enam, M.A.;Srivastava, S.;Knijnenburg, B.P.
• 通讯作者: Knijnenburg, B.P.

The Diversity of Music Recommender Systems

音乐推荐系统的多样性

• DOI: 10.1145/3490100.3516474
• 发表时间: 2022
• 期刊: IUI '22 Companion: 27th International Conference on Intelligent User Interfaces
• 作者: Baracskay, Ian;Baracskay III, Donald J;Iqbal, Mehtab;Knijnenburg, Bart Piet
• 通讯作者: Knijnenburg, Bart Piet