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小时的现状，在那里，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、浓缩服务提供者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。该公司的一项关键发明是自动时间表推荐系统，该系统为一个或多个孩子的家庭推荐课程时间表，满足年龄和驾驶距离等限制，最大限度地提高理想属性，如最少的驾驶和跨时间的课程多样性，并利用历史数据，如过去的课程和社交数据，如朋友和同学喜欢的课程，做出有效的推荐。拟议中的技术消除了家长在购买强化课程时最重要的摩擦，即从不同供应商的课程中拼凑出一个课程时间表。该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起来解决问题并实现可扩展性和效率的跨学科研究。这个小企业创新研究（SBIR）第一阶段项目将设计、实施和评估一个自动时间表推荐系统。现有的推荐系统是不够的，因为它们主要是向单个用户推荐单个项目；此应用程序需要在一组用户（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，适合连贯时间表的一系列课程）。现有的课程调度算法是不够的，因为他们找到最优调度使用已知的偏好；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，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，以提出最适合家长并满足多维约束的完整时间表。由此产生的系统将部署给该公司的用户，并对其功效进行广泛的评估。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.