CRII: CHS: Recommender Systems for Self-Actualization

CRII：CHS：自我实现推荐系统

• 批准号: 1565809
• 负责人: Bart Knijnenburg
• 金额: $ 17.5万
• 依托单位: Clemson University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2019-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1565809&HistoricalAwards=false
• 关键词: CRII; CHS; Recommender; Systems; Self

Every day, we are confronted with an abundance of decisions that require us to choose from a seemingly endless number of choice options. Recommender systems are supposed to help us deal with this formidable task, but some scholars claim that these systems instead put us inside a "Filter Bubble" that severely limits our perspectives. This project investigates a new direction for recommender systems research with the main goal of supporting users in developing, exploring, and understanding their unique personal preferences. The project will develop a new type of recommender system: a Recommender System for Self-Actualization (RSSA), which will support decisions that have a resounding influence on our life (e.g., choosing an education or a job) where we do not want to just take the easiest option, but rather to develop ourselves into unique individuals with a strong sense of determination that we have chosen the right path.In contrast to most existing recommender systems research, this project carefully considers the psychology of consumer choice processes and develops new types of recommendations to support these processes in novel ways (the RSSA features). The PI's team will develop a new recommender system outfitted with the capability to display a traditional Top-N recommendation list, plus each new RSSA feature. The team will then test the benefit of each RSSA feature against traditional Top-N recommendations in an online user experiment. This benefit will be measured by participants' subjective evaluation of the system and their usage behavior logs.

每天，我们都面临着大量的决定，这些决定要求我们从看似无穷无尽的选择中进行选择。推荐系统本应帮助我们处理这项艰巨的任务，但一些学者声称，这些系统反而将我们置于严重限制我们视角的“过滤泡泡”中。这个项目探索了推荐系统研究的新方向，主要目标是支持用户开发、探索和理解他们独特的个人偏好。该项目将开发一种新型的推荐系统：自我实现推荐系统(RSSA)，它将支持对我们的生活产生重大影响的决策(例如，选择教育或工作)，在这些决策中，我们不想只选择最简单的选择，而是将自己发展成具有强烈决心的独特个体，相信我们选择了正确的道路。与大多数现有的推荐系统研究不同，该项目仔细考虑消费者选择过程的心理，并开发新类型的推荐，以新颖的方式支持这些过程(RSSA功能)。PI的团队将开发一种新的推荐系统，该系统配备有能力显示传统的Top-N推荐列表，以及每个新的RSSA功能。然后，该团队将在一项在线用户实验中，对照传统的Top-N推荐来测试每个RSSA功能的好处。这一好处将通过参与者对系统的主观评价和他们的使用行为日志来衡量。

项目成果

Recommender Systems for Self-Actualization

• DOI: 10.1145/2959100.2959189
• 发表时间: 2016-09
• 期刊: Proceedings of the 10th ACM Conference on Recommender Systems
• 作者: Bart P. Knijnenburg;S. Sivakumar;Daricia Wilkinson

Testing a Recommender System for Self-Actualization

测试自我实现的推荐系统

• 发表时间: 2017
• 期刊: Proceedings of the Second Workshop on Engineering Computer-Human Interaction in Recommender Systems co-located with the 9th ACM SIGCHI Symposium on Engineering Interactive Computing Systems
• 作者: Wilkinson, Daricia;Sivakumar, Saadhika;Sinha, Pratitee;Knijnenburg, Bart P.
• 通讯作者: Knijnenburg, Bart P.