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.

该项目旨在以一种名为“以网络为中心的推荐系统”的建议来推进对新观点的研究。与他人共享信息是社交媒体活动的关键驱动力。 但是，令人惊讶的是，人们对人们如何在分享和阅读的内容或与谁分享和关注谁的选择中相对较少了解。 与传统的电子商务和研究环境不同，在给定的时刻，以网络为中心的观点将推荐视为一种动态的社会过程，这些环境和研究环境将针对个人进行准确的预测。 更多地了解影响建议过程的因素，将对社交媒体平台和推荐系统的设计产生实际影响，从而导致工具更好地服务于共享信息的个人和社会目标。 它还将为了解信息在社交网络中的传播如何比目前未能解释这些因素的当前扩散模型深，允许科学家建立更准确，更一般的共享行为模型。一系列实验室实验会要求人们彼此提出建议并采取建议。 这些实验将使用真实的社交媒体数据，例如Facebook Likes和Twitter主题标签的使用，但是控制算法，接口和合作伙伴在实验中与之互动。 这样做将有助于权衡影响人们共享和阅读行为的因素的重要性。 在这些因素中，用户如何受到诸如自我表达和帮助人的目标的影响，诸如对共享项目的过滤算法和社会解释之类的系统特征，以及共享者和接收者之间的相似性和信任等社会力量。