Algorithms for Pathway Operations: Theory and Application to Museums

路径操作算法：理论及其在博物馆中的应用

• 批准号: EP/Y003721/1
• 负责人: Mohammed Aouad
• 金额: $ 128.62万
• 依托单位: London Business School
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY003721%2F1
• 关键词: Algorithms; Pathway; Operations; Theory; Application

With the growth of marketplaces and smart supply chains, there has been unprecedented research on data-driven operations. Algorithms for resource allocation and personalisation allow us to match supply with demand more efficiently in industries such as retail, transportation, and healthcare. Yet, these algorithms generally optimise for a single user-platform interaction, e.g., a purchase decision in a given product category. A major issue for platform operations is to develop a holistic viewpoint on user experience, supported by new data sources tracking longitudinal behaviour such as mobile trace data and clickstream data. Thus, I propose to design algorithms that optimise engagement for repeated user interactions in multi-touch operational platforms. As a case study, I will explore a highly relevant, yet uncharted application of "pathway operations": visitor experience in art institutions such as museums and galleries across physical and digital layouts. My research work will range from theoretical advancements in algorithm design to applied work in collaboration with museums. First, I will develop a new predictive utility maximisation framework for pathway choice models, which can be estimated from trajectory-based pathway data. Second, I will study the design and rigorous analysis of algorithms incorporating pathway choice models at their core, including novel formulations of assortment, online matching and multi-armed bandit problems. Third, building on an ongoing collaboration with a large museum, I will turn to simulations and implementations of these algorithms together with researchers in empirical fields. We will generate open-source code to help institutions like museums craft new experiences for their visitors, ranging from recommended physical routes to personalised virtual exhibitions. This research work will contribute to broader initiatives for an interactive, non-invasive and museum-centric digital ecosystem for our artistic heritage.

随着市场和智能供应链的发展，对数据驱动运营的研究前所未有。资源分配和个性化算法使我们能够在零售、运输和医疗保健等行业更有效地匹配供需。然而，这些算法通常针对单个用户-平台交互进行优化，例如，在给定的产品类别中的购买决定。平台运营的一个主要问题是，在跟踪纵向行为的新数据源（如移动的跟踪数据和点击流数据）的支持下，对用户体验形成一种整体观点。因此，我建议设计的算法，优化重复的用户交互在多点触摸操作平台的参与。作为一个案例研究，我将探索一个高度相关的，但未知的“路径操作”的应用：在艺术机构，如博物馆和画廊跨越物理和数字布局的游客体验。我的研究工作将从算法设计的理论进步到与博物馆合作的应用工作。首先，我将开发一个新的预测效用最大化框架的途径选择模型，这可以从基于概率的途径数据估计。其次，我将研究算法的设计和严格的分析，将路径选择模型作为其核心，包括分类，在线匹配和多臂强盗问题的新配方。第三，在与一家大型博物馆正在进行的合作的基础上，我将与经验领域的研究人员一起转向这些算法的模拟和实现。我们将生成开源代码，帮助博物馆等机构为游客打造新的体验，从推荐的物理路线到个性化的虚拟展览。这项研究工作将有助于为我们的艺术遗产建立一个互动的、非侵入性的、以博物馆为中心的数字生态系统。