CHS: Small: Recommender and Decision-Making Systems Seeking Compatible Sets from Multiple Collections under Varying Constraints

帮助决策的推荐系统越来越多地被用于许多信息环境中。这一领域的大多数研究都集中在算法上，这些算法根据用户自己和他人过去的偏好来预测用户对某一商品的偏好程度。这类研究倾向于将问题视为机器学习、统计或优化问题；然而，这抽象了使推荐系统对人们有用的重要方面，包括关注决策过程和所支持的上下文。该项目将开发以智能家居为驱动域的推荐系统，明确关注这些问题。在这种情况下开发的推理方法可以为其他领域的类似决策问题提供参考，例如管理用品和后勤以及获取信息。该项目将为推荐系统提出一些重要但未得到充分解决的挑战的解决方案，包括：(1)推荐一组协同工作良好的相关物品，而不是单独的物品；(2)主要从现有物品中推荐和重新推荐物品，而不是关注新物品的一次性消费；(3)考虑影响决策的物质和社会环境的重要因素。该项目将在智能家居的整体选择决策的背景下探索这些问题。该系统将以基于案例的推理为基础；这将缓解困扰基于评级的协作过滤算法的冷启动问题，并与项目、偏好和上下文的计划基于属性的表示很好地匹配。为了做到这一点，该团队将使用3D扫描仪和渲染软件来创建物品的个性化模型，然后专家和众筹人员将考虑到社会和物理环境来评估模型输出。这将生成一个案例库，与组合项目的规则集一起，可用于为项目集和与已选择的项目很好地匹配的单个项目生成推荐。该系统将基于现有的原型，改装成在平板电脑上运行，以支持远程数据收集。部署包括一个设置过程，团队将在物品上贴上RFID标签并拍照，以供以后分析和添加属性；三个月的时间段，在此期间将记录用户的选择和背景，但不会提出任何建议；以及六个月的时间段，在此期间提供建议。该系统将根据推荐的有用性、它们建议新颖和受欢迎的组合的能力以及它们对用户的使用和重组的影响进行评估？现有项目。通过在家庭中部署原型系统，这项工作还将使人们深入了解设计智能家居应用程序，如涉及对象跟踪的活动和药物监控。

Recommender systems that assist in decision-making are increasingly used in a number of information settings. Most research in this area focuses on algorithms that predict how much a user will prefer a given item based on their own and others' past preferences. Such research tends to treat the question as a machine learning, statistical, or optimization problem; however, this abstracts away important aspects of making recommender systems useful to people, including attending to the decision-making processes and contexts being supported. This project will develop recommender systems that explicitly attend to these concerns using a smart home as the driving domain. The methods developed for reasoning in this context can inform similar decision problems in other domains, such as managing supplies and logistics and accessing information. This project will develop solutions to a number of important but under-addressed challenges for recommender systems, including (1) recommending groups of related items that work well together, rather than individual items; (2) recommending and re-recommending items primarily from an existing set of items rather than focusing on single-use consumption of new items; and (3) considering important factors about both the physical and social environment that affect decision making. The project will explore these issues in the context of ensemble selection decisions in a smart home. The system will be grounded in case-based reasoning; this will mitigate the cold start problem that plagues ratings-based collaborative filtering algorithms and fits well with the planned attribute-based representations of items, preferences, and context. To do this, the team will use 3D scanners and rendering software to create personalized models of items, and both experts and crowdworkers will then assess the model outputs, accounting for social and physical contexts. This will generate a case library that, along with rulesets for combining items, can be used to generate recommendations for both sets of items and individual items that fit well with already-chosen ones. The system will be based on an existing prototype, adapted to run on a tablet to support remote data collection. The deployment includes a setup process where the team will attach RFID tags to items and photograph them for later analysis and addition of attributes; a three-month period in which users' choices and context will be logged but no recommendations are made, and a six-month period where recommendations are offered. The system will be evaluated in terms of the usefulness of the recommendations, their ability to suggest novel and liked combinations, and their effect on the use and recombination of users? existing items. Through deploying prototype systems in homes, the work will also give insight into designing smart home applications such as activity and medication monitoring that involve object tracking.