标题： 偏好为中心的路径推荐：均衡、学习与可证明的效率 显示英文标题

标题： Preference-Centric Route Recommendation: Equilibrium, Learning, and Provable Efficiency

摘要： 传统建模和预测交通行为的方法通常依赖于沃罗基平衡（WE），假设非原子性的交通需求，并忽略个体决策之间的相关性。然而，实时人类反馈和自适应推荐系统的作用日益增强，这需要更具表达力的均衡概念，以更好地捕捉用户偏好和路由行为的随机特性。 本文引入了一个以用户偏好为中心的路由推荐框架，该框架基于博尔达粗相关均衡（BCCE）的概念，在此框架下，当用博尔达评分——即成对比较来编码用户偏好时，用户没有偏离推荐策略的动机。 我们开发了一种从对决反馈中学习的自适应算法，并表明它实现了$\mathcal{O}(T^{\frac{2}{3}})$后悔值，这意味着在温和假设下收敛到 BCCE。 我们通过案例研究进行了实证评估，以说明和验证我们的理论分析。 结果证明了我们方法的有效性和实际相关性。 显示英文摘要

摘要： Traditional approaches to modeling and predicting traffic behavior often rely on Wardrop Equilibrium (WE), assuming non-atomic traffic demand and neglecting correlations in individual decisions. However, the growing role of real-time human feedback and adaptive recommendation systems calls for more expressive equilibrium concepts that better capture user preferences and the stochastic nature of routing behavior. In this paper, we introduce a preference-centric route recommendation framework grounded in the concept of Borda Coarse Correlated Equilibrium (BCCE), wherein users have no incentive to deviate from recommended strategies when evaluated by Borda scores-pairwise comparisons encoding user preferences. We develop an adaptive algorithm that learns from dueling feedback and show that it achieves $\mathcal{O}(T^{\frac{2}{3}})$ regret, implying convergence to the BCCE under mild assumptions. We conduct empirical evaluations using a case study to illustrate and justify our theoretical analysis. The results demonstrate the efficacy and practical relevance of our approach.