III: Small: Moving offline learning to rank online, from theory to practice

在线学习排名是一种现代机器学习技术，它在与最终用户交互时自适应地提高结果排名。例如，当应用于搜索引擎系统中时，在线学习排名解决方案可以通过重复三个步骤来估计结果的最佳排名：呈现排名列表、收集用户反馈(例如，点击)、然后为下一轮交互更新排名。然而，大多数现有的在线学习排名解决方案都是从最初为在线优化设计的算法扩展而来的，而不是排名问题；因此，它们的实际性能往往比离线同行差得多。这直接限制了他们的实际接受度。该项目旨在开发一个全新的在线学习排名框架，它直接将线下学习的最佳实践转换为在线排名，以提高绩效和可证明的保证。该项目的关键创新是将指数级大的排名空间动态地打破为二次大小的两两比较，其中在线学习只对不确定的实例排名对执行。基于这一新的在线结对学习策略，本项目研究多目标优化、协作和联合学习，使在线学习在更广泛的应用场景中进行排名，例如公平和个性化的在线学习排名。研究成果，包括开发的算法、试验床和评估方案，将通过开放源代码库传播。研究活动将纳入教材，供学生在信息检索和机器学习领域进行培训和教育。计划向高中生推广有关在线信息技术、隐私和公平的教育，将提高他们对在线系统中隐私被侵犯和不公平的潜在风险的认识。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。

Online learning to rank is a modern machine learning technique that adaptively improves result rankings during its interactions with end users. For example, when applied in a search engine system, an online learning to rank solution can estimate the optimal ranking of results by repeating three steps: present a ranked list, collect user feedback (e.g., clicks), then update the ranking for next round of interaction. However, most existing online learning to rank solutions are extended from algorithms originally designed for online optimization, rather than the ranking problem; and thus, their practical performance is often much worse than their offline counterparts. This directly limits their practical acceptance. This project aims to develop a completely new online learning to rank framework, which directly converts the best practices in offline learning to rank online for improved performance and provable guarantees. The key innovation of this project is to break the exponentially large ranking space into quadratic-size pairwise comparisons on the fly, where online learning is only performed on the uncertain pairs of instance rankings. Built on this new online pairwise learning strategy, this project studies multi-objective optimization, collaborative and federated learning to enable online learning to rank in a wider range of application scenarios, such as fair and personalized online learning to rank. The research outcomes, including the developed algorithms, testbeds and evaluation protocols, will be disseminated via an open-source library. The research activities will be incorporated into teaching materials for student training and education in the area of information retrieval and machine learning. The planed outreach to high school students for education about online information techniques, privacy and fairness will increase their awareness of potential risk of privacy breaches and unfairness in online systems.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.