个性化和情景感知的多模式路线推荐：FAVOUR算法
Personalized and situation-aware multimodal route recommendations: the FAVOUR algorithm
摘要：
多模态网络中的路径选择在不同的个体之间以及当前的情境背景下表现出相当大的差异。推荐算法的个性化在许多领域已经很常见，例如，网上零售然而，大多数在线路线应用程序仍然只提供最短距离或最短旅行时间的路线，忽略了个人偏好以及当前情况。这两个方面在多模式环境中特别重要，因为某些交通方式（如骑自行车）的吸引力主要取决于个人特征和天气等外部因素。本文介绍了最喜爱的路线推荐（FAVOUR）的方法，提供个性化的，情况感知的路线建议的基础上三个步骤：首先，在初始化阶段，用户提供有限的信息（家庭位置，工作地点，移动选项，社会人口统计）用于选择一个出少量的初始配置文件。第二，根据这些信息，一个声明的偏好调查的设计，以锐化的配置文件。在该步骤中，使用大规模偏好先验来对来自步骤一中识别的类的偏好的先验知识进行编码。第三，随后在路由服务的使用期间持续更新简档。最后两个步骤使用贝叶斯学习技术，以整合来自所有贡献个体的信息。FAVOUR方法被详细介绍，并在少数调查参与者身上进行了测试。在这个真实世界数据集上的实验结果表明，FAVOUR生成的推荐质量更好。从文献中的替代学习算法。特别是质量偏好的定义之前，初始化的步骤二，提供更好的预测比一些替代品从文献中。
英文摘要：
Route choice in multimodal networks shows a considerable variation between different individuals as well as the current situational context. Personalization of recommendation algorithms are already common in many areas, e.g., online retail. However, most online routing applications still provide shortest distance or shortest travel-time routes only, neglecting individual preferences as well as the current situation. Both aspects are of particular importance in a multimodal setting as attractivity of some transportation modes such as biking crucially depends on personal characteristics and exogenous factors like the weather. This paper introduces the FAVourite rOUte Recommendation (FAVOUR) approach to provide personalized, situation-aware route proposals based on three steps: first, at the initialization stage, the user provides limited information (home location, work place, mobility options, sociodemographics) used to select one out of a small number of initial profiles. Second, based on this information, a stated preference survey is designed in order to sharpen the profile. In this step a mass preference prior is used to encode the prior knowledge on preferences from the class identified in step one. And third, subsequently the profile is continuously updated during usage of the routing services. The last two steps use Bayesian learning techniques in order to incorporate information from all contributing individuals. The FAVOUR approach is presented in detail and tested on a small number of survey participants. The experimental results on this real-world dataset show that FAVOUR generates better-quality recommendations w.r.t. alternative learning algorithms from the literature. In particular the definition of the mass preference prior for initialization of step two is shown to provide better predictions than a number of alternatives from the literature.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
