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，具有众多实际应用场景。当考虑优化多个用户的排队时间与人群密度，以及涉及景点受欢迎程度、排队时间、步行时间、营业时间等多个参数时，该任务更具挑战性。现有解决方案通常聚焦于单用户视角，未能解决由自然人群行为引发的现实问题（如自私路由问题）。本文提出策略性与人群感知的行程推荐算法（Strategic and Crowd-Aware Itinerary Recommendation, SCAIR），该算法在真实环境中优化群体效用。我们将路线推荐策略建模为马尔可夫决策过程，并提出一种状态编码机制，实现线性时间内实时规划与分配。利用主题公园数据集，我们将算法与多种竞争性及现实性基线方法进行对比评估，结果表明SCAIR在四个主题公园中均能有效解决自私路由问题，性能优于这些基线方法。