SBIR Phase I: 6crickets Automatic Schedule Recommendation

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力将是打破每个孩子每年1000小时的美国孩子没有学校，没有父母，应该获得最好的丰富活动。该公司计划为学校，丰富供应商和家长建立急需的技术基础设施。通过自动化和简化供应商供应，家长消费和学校举办丰富活动的过程，拟议的系统将消除重大摩擦，创造一个有效的市场。一个关键的发明是该公司的自动日程推荐系统，该系统为一个或多个孩子的家庭推荐课程日程，并满足年龄和驾驶距离等约束条件，最大化理想的属性，如最小驾驶和跨时间的课程多样性，并利用历史数据，如过去参加的课程和社交数据，如朋友和同学喜欢的课程，提出有效的建议。拟议中的技术消除了家长购买丰富课程期间最重要的摩擦，即将不同提供商的课程拼凑在一起。该项目是第一个跨学科的研究，战略性地结合了优化和推荐系统领域的算法，以解决问题，并实现可扩展性和效率。这个小企业创新研究（SBIR）第一阶段项目将设计，实现和评估自动日程推荐系统。现有的推荐系统是不够的，因为它们主要向单个用户推荐单个项目;该应用程序需要在一组用户（即同一家庭或朋友组中的儿童）之间推荐和协调一系列项目（即适合连贯时间表的一系列课程）。现有的类调度算法是不够的，因为他们发现最佳的时间表使用已知的偏好，在这个问题中，有必要预测偏好。该公司将设计一个全自动日程推荐系统，该系统使用机器学习技术预测每个会话对每个用户的适用性，并随着时间的推移汇总这些预测，以提出最适合父母并满足多维约束的完整日程。最终的系统将被部署到该公司的用户沿着其功效的广泛评估。该奖项反映了NSF的法定使命，并已被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will be to disrupt the 1000 hours per child per year where American children have no school and no parents and deserve access to the best enrichment activities. The company plans to build a much-needed technical infrastructure for schools, enrichment providers, and parents. By automating and simplifying the process of providers supplying, parents consuming and schools hosting enrichment activities, the proposed system will remove significant friction and create an efficient market. A key invention is the company's Automatic Schedule Recommendation System which recommends a schedule of classes for a family of one or more children and satisfies constraints such as ages and driving distance, maximizes desirable attributes such as minimal driving and diversity in classes across time, and leverages historical data like classes taken in the past and social data such as classes enjoyed by friends and classmates, to make effective recommendations. The proposed technology removes the most significant friction during parents' purchases of enrichment classes, namely, piecing together a class schedule from classes across different providers. This project is the first interdisciplinary research that strategically combines algorithms from the fields of optimizations and recommender systems to solve the problem and achieve scalability and efficiency.This Small Business Innovation Research (SBIR) Phase I project will design, implement and evaluate an Automatic Schedule Recommendation System. Existing recommender systems are insufficient because they largely recommend a single item to a single user; this application requires that a sequence of items (namely, a sequence of classes that fit in a coherent schedule) be recommended and coordinated among a group of users (i.e. children in the same family or set of friends). Existing class scheduling algorithms are insufficient because they find optimal schedules using known preferences; in this problem there is a need to predict preferences. The company will design a fully-automatic schedule recommendation system that predicts the suitability of each session to each user using machine learning techniques and aggregates these predictions over time to propose full schedules that are most appropriate for a parent and satisfy multi-dimensional constraints. The resulting system will be deployed to the company's users along with extensive evaluation of its efficacy.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.