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:旅行计划推荐是一个具有数不清的实际应用的复杂序列预测问题。当考虑到多个用户排队时间和拥挤水平的优化以及许多涉及参数时,如景点受欢迎程度、排队时间、步行时间和运营时间时,这个任务变得更加具有挑战性。现有解决方案通常集中在单个人的观点上,并且未能解决自然人群行为所导致的现实问题,例如自私路由问题。在本文中,我们介绍了战略和众人感知的行程推荐(SCAIR)算法,该算法在实际环境中优化了群组效用。我们将路线推荐策略建模为马尔可夫决策过程,并提出了一种状态编码机制,该机制使实时规划和分配在线性时间内实现。我们使用主题公园数据集对我们的算法进行评估,评估结果表明,SCAIR在解决四个主题公园的自私路由问题方面优于各种竞争且现实的基线模型。