CHS: Medium: Immersive Recommendation Systems: User-Centric Recommendation Models and Applications

这项工作致力于创造一个超个性化内容、数字服务和个人信息管理工具的未来，让个人更直接、更有选择性、更透明地从他们生成的数据中受益。然后，个人将能够深入了解自己的行为，个性化自己的体验，并最终更有效地利用服务来实现他们的目标。此外，让最终用户参与其生成的数据的系统可以促进个人信息的本地处理和选择性共享。鉴于在线工具在人们的工作、个人和社交生活中的普遍性，他们采取的行动越来越多地受到推荐系统的影响。 今天的个性化和推荐方法是以提供商为中心的。 从消费者的角度对个性化和推荐进行更广泛的探索，整个社会都将从中受益。此外，沿着这些路线，人们越来越关注对个人控制数据共享的期望的变化。该项目以用户为中心和个人共享策略感知的设计是解决提供商和用户之间这种紧张关系的潜在解决方案，允许人们更直接地从他们的数据中受益。研究目标是开发新颖的用户建模技术，策略感知系统和丰富的用户交互，允许个人利用自己的多样化数字痕迹（“小数据”），启用新颖的应用程序，并在限制隐私暴露的同时接收更多个人相关的推荐。这项研究将有助于新的用户建模和交互技术，需要把个人在他们的个性化的中心，特别是：（1）沉浸式用户建模技术，分析不同类型的用户数据，包括社会媒体流，私人文本通信，网页浏览，地理位置跟踪，和个人图像，以纳入用户的多样化和特殊的利益。(2)新颖的推荐模型和策略感知软件架构，由开源构建块组成，旨在促进这种方法的推广，以摄取不同的个人数据跟踪并为不同的应用目标提供支持。(3)通过用户体验的参与式设计，以及对部署的系统和应用程序的定性和定量评估，了解和解决沉浸式推荐系统中以人为本的关键挑战的方法。

This work strives to create a future in which hyper-personalized content, digital services, and personal information management tools let individuals benefit from the data they generate more directly, selectively, and transparently. Individuals will then be empowered to gain insights into their own behavior, personalize their own experiences, and ultimately more effectively utilize the services to achieve their goals. Moreover, the systems that engage end-users with the data they generate can promote local processing and selective sharing of personal information. Given the pervasiveness of online tools in a person's work, personal, and social life, the actions they take are increasingly shaped by recommendation systems. Today's approaches to personalization and recommendation are provider centric. Society as a whole will benefit from broader exploration of personalization and recommendation from the consumers' perspective. Also along these lines, there is increasing concern about the shifts in expectations for individual control over data sharing. This project's user-centered and personal-sharing-policy-aware design is a potential solution to address this tension between providers and users, allowing people to more directly benefit from their data.The research objective is to develop novel user modeling techniques, policy-aware systems, and rich user interactions that allow individuals to harness their own diverse digital traces ("small data"), enable novel applications, and receive more personally-relevant recommendations while limiting privacy exposure. This research will contribute the novel user modeling and interaction techniques needed to put the individual at the center of their personalization, in particular: (1) Immersive user modeling techniques that analyze diverse types of user data, including social media streams, private text communications, web browsing, geo-location traces, and personal images, to incorporate users' diverse and idiosyncratic interests. (2) Novel recommendation models and policy-aware software architecture that consists of open source building blocks designed to facilitate generalization of this approach to ingest diverse personal data traces and feed diverse application targets. (3) Methods to understand and address key human-centered challenges in immersive recommender systems through participatory design of the user experience, as well as qualitative and quantitative evaluation of deployed systems and applications.