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

辅助决策的推荐系统越来越多地用于许多信息设置。该领域的大多数研究都集中在算法上，该算法可以根据用户自己和他人过去的偏好来预测用户对给定商品的偏好程度。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，包括参与决策过程和所支持的背景。该项目将开发推荐系统，以智能家居为驱动领域，明确解决这些问题。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信息，例如管理供应和物流以及访问信息。该项目将针对推荐系统面临的一些重要但未得到充分解决的挑战开发解决方案，包括(1)推荐协同工作良好的相关项目组，而不是单个项目；(2)主要从现有的一组商品中推荐和重新推荐商品，而不是专注于新商品的一次性消费；(3)综合考虑影响决策的自然环境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讨这些问题。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同过滤算法的冷启动问题，并与计划的基于属性的项目、偏好和上下文表示非常适合。为了做到这一点，团队将使用3D扫描仪和渲染软件来创建个性化的物品模型，然后专家和众包工作者将评估模型输出，考虑到社会和物理环境。这将生成一个案例库，它与用于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建议。该系统将以现有的原型为基础，适应在平板电脑上运行，以支持远程数据收集。部署包括一个设置过程，在这个过程中，团队将把RFID标签贴在物品上，并对它们进行拍照，以供以后分析和添加属性；在三个月的时间里，用户的选择和背景将被记录下来，但不会提出建议；在六个月的时间里，会提出建议。该系统将根据推荐的有用性、它们提出新颖和喜欢的组合的能力以及它们对用户使用和重新组合的影响来评估。现有的项目。通过在家庭中部署原型系统，这项工作还将为设计智能家居应用提供见解，例如涉及对象跟踪的活动和药物监测。

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.