CHS: Small: How recommendation and explanation affect preferences in social networks

CHS：小：推荐和解释如何影响社交网络中的偏好

• 批准号: 1422484
• 负责人: Malte Jung
• 金额: $ 19.02万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2019-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1422484&HistoricalAwards=false
• 关键词: CHS; Small; How; recommendation; explanation

This project seeks to advance research on a novel perspective on recommendation called network-centric recommender systems. Sharing information with others is a key driver of social media activity. But, surprisingly, relatively little is known about how people make choices around what to share and to read, or whom to share with and pay attention to. Unlike traditional e-commerce and research contexts that focus on accurate prediction for individuals at a given moment, the network-centric perspective sees recommendation as a dynamic social process. Understanding more about the factors that influence recommendation processes will have practical impact on the design of social media platforms and recommender systems, leading to tools that better serve both individual and societal goals around sharing information. It will also set the stage for an understanding of how information spreads in social networks that is deeper than current diffusion models that fail to account for these factors, allowing scientists to build more accurate and more general models of sharing behavior.A series of laboratory experiments will ask people to make and take recommendations from each other. These experiments will use real social media data such as Facebook Likes and Twitter hashtag usage, but control the algorithms, interfaces, and partners people interact with in the experiment. Doing this will help weigh the importance of factors that influence people's sharing and reading behavior. Among these factors are how users are influenced by goals such as self-expression and helping people, system characteristics such as filtering algorithms and social explanations for shared items, and social forces such as similarity and trust between a sharer and a receiver.

本项目旨在推进对以网络为中心的推荐系统这一新的推荐视角的研究。与他人分享信息是社交媒体活动的关键驱动力。但是，令人惊讶的是，关于人们如何选择分享和阅读什么，或者与谁分享和关注什么，人们知道的相对较少。与传统的电子商务和研究环境不同，传统的电子商务和研究环境专注于对给定时刻的个人进行准确预测，以网络为中心的视角将推荐视为一个动态的社交过程。更多地了解影响推荐过程的因素将对社交媒体平台和推荐系统的设计产生实际影响，从而产生更好地服务于个人和社会共享信息目标的工具。它还将为理解信息如何在社交网络中传播奠定基础，比目前未能考虑这些因素的现有扩散模型更深入，从而使科学家能够建立更准确、更通用的分享行为模型。一系列实验室实验将要求人们相互推荐和采纳。这些实验将使用真实的社交媒体数据，如Facebook赞和Twitter标签使用情况，但控制实验中人们互动的算法、界面和合作伙伴。这样做将有助于权衡影响人们分享和阅读行为的因素的重要性。这些因素包括用户如何受到自我表达和帮助他人等目标的影响；系统特征，如对共享项目的过滤算法和社交解释；以及社会力量，如共享者和接受者之间的相似性和信任度。