Itinerary recommendation is a complex sequence prediction problem with numerous real-world applications. This task becomes even more challenging when considering the optimization of multiple user queuing times and crowd levels, as well as numerous involved parameters, such as attraction popularity, queuing time, walking time, and operating hours. Existing solutions typically focus on single-person perspectives and fail to address real-world issues resulting from natural crowd behavior, like the Selfish Routing problem. In this paper, we introduce the Strategic and Crowd-Aware Itinerary Recommendation (SCAIR) algorithm, which optimizes group utility in real-world settings. We model the route recommendation strategy as a Markov Decision Process and propose a State Encoding mechanism that enables real-time planning and allocation in linear time. We evaluate our algorithm against various competitive and realistic baselines using a theme park dataset, demonstrating that SCAIR outperforms these baselines in addressing the Selfish Routing problem across four theme parks.
翻译:行程推荐是一个复杂的序列预测问题,具有多种实际应用。当考虑到多个用户排队时间和拥挤程度的优化时,这项任务变得更加具有挑战性,以及诸多相关参数,如景点受欢迎程度、排队时间、步行时间和开放时间等。现有解决方案通常聚焦于个人观点,未能解决自私路由问题等众多来源于自然人群行为的实际问题。在本文中,我们引入了一种名为"策略性、人群感知"的行程推荐算法(SCAIR),该算法能够在实际环境中优化团队效用。我们将路线推荐策略建模为马尔科夫决策过程,并提出了一种状态编码机制,以线性时间实现实时规划和分配。通过使用主题公园数据集对算法进行各种竞争和现实基线的评估,我们证明了SCAIR在解决自私路由等问题时,优于这些基线,且具有可扩展性。