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/商业潜力将打破每个儿童每年1000小时的生活，美国儿童没有学校，没有父母，应该获得最好的丰富活动。该公司计划为学校、培训提供商和家长建立亟需的技术基础设施。通过自动化和简化供应商供应、家长消费和学校主办丰富活动的流程，拟议的系统将消除重大摩擦，并创造一个有效的市场。该公司的一项关键发明是自动时间表推荐系统，该系统可以为一个或多个孩子的家庭推荐课程时间表，并满足年龄和开车距离等限制，最大限度地提高理想的属性，如最小限度的驾车和不同时间的课程多样性，并利用历史数据(如过去上的课)和社交数据(如朋友和同学的课程)来做出有效的推荐。这项拟议的技术消除了家长在购买强化课程时最重要的摩擦，即从不同供应商的班级拼凑出课程表。这个项目是第一个将优化和推荐系统领域的算法战略性地结合在一起来解决问题并实现可伸缩性和效率的跨学科研究。这个小型企业创新研究(SBIR)第一阶段项目将设计、实施和评估一个自动日程推荐系统。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；该应用要求在一组用户(即，同一家庭或一组朋友中的孩子)之间推荐和协调一系列项目(即，符合连贯时间表的一系列课程)。现有的排课算法是不够的，因为它们使用已知的偏好来寻找最优的排课；在这个问题中，需要预测偏好。该公司将设计一个全自动时间表推荐系统，使用机器学习技术预测每个会话对每个用户的适宜性，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时间表。最终的系统将被部署给公司的用户，并对其效果进行广泛的评估。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.