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

该项目旨在推进一个新的角度推荐称为网络为中心的推荐系统的研究。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，人们对如何选择分享和阅读什么，或者与谁分享和关注谁知之甚少。 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在给定时刻对个人进行准确的预测，以网络为中心的观点将推荐视为一个动态的社会过程。 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，这比目前未能考虑这些因素的传播模型更深入，使科学家能够建立更准确和更通用的共享行为模型。 这些实验将使用真实的社交媒体数据，如Facebook Likes和Twitter hashtag使用情况，但控制实验中人们互动的算法，界面和合作伙伴。 这样做有助于衡量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目标的影响，如自我表达和帮助他人，系统特征，如过滤算法和共享项目的社会解释，以及社会力量，如共享者和接收者之间的相似性和信任。

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.