Road-based travel recommendation using geo-tagged images
Road-based travel recommendation using geo-tagged images
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DOI：
10.1016/j.compenvurbsys.2013.07.006
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发表时间：
2015-09
期刊：
影响因子：
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通讯作者：
Yeran Sun;H. Fan;M. Bakillah;A. Zipf
中科院分区：
文献类型：
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作者：
Yeran Sun;H. Fan;M. Bakillah;A. Zipf
Flickr等社交媒体上带有地理标记的照片明确显示了游客的轨迹。它们可以用来揭示游客对地标和旅游路线的偏好。现有的大多数路线搜索工作都是基于支持GPS的设备用户的轨迹。从一个不同的角度出发，我们尝试提出一种新的路径选择方法，其基本路径单元是单独的路段，而不是GPS轨迹段。在本文中，我们构建了一个推荐系统，为用户提供最受欢迎的地标以及地标之间的最佳旅行路线。通过使用Flickr地理标签照片，可以识别出一个城市排名最靠前的旅游目的地，然后推荐出热门旅游目的地之间的最佳旅行路线。我们应用空间聚类方法来识别主要的旅游地标，并随后对这些地标进行排名。使用机器学习方法，根据相关参数，如用户数量和兴趣点数量，计算该道路的旅游热度。这些受欢迎程度评估被集成到路线推荐系统中。路线推荐系统同时考虑了受欢迎程度评估和道路长度。推荐给用户的最佳路线将距离最小化，同时包含最大的旅游热度。实验是在两种不同的情况下进行的。实验结果表明，该推荐系统能够为用户提供良好的出行规划，包括城市内的顶级地标和合适的路线。此外，系统还为推荐路线提供用户生成的语义信息。
Geotagged photos on social media like Flickr explicitly indicate the trajectories of tourists. They can be employed to reveal the tourists’ preference on landmarks and routings of tourism. Most of existing works on routing searches are based on the trajectories of GPS-enabled devices’ users. From a distinct point of view, we attempt to propose a novel approach in which the basic unit of routing is separate road segment instead of GPS trajectory segment. In this paper, we build a recommendation system that provides users with the most popular landmarks as well as the best travel routings between the landmarks. By using Flickr geotaggged photos, the top ranking travel destinations in a city can be identified and then the best travel routes between the popular travel destinations are recommended. We apply a spatial clustering method to identify the main travel landmarks and subsequently rank these landmarks. Using machine learning method, we calculate the tourism popularity of the road in terms of relevant parameters, e.g., the number of users and the number of Point-of-Interests. These popularity assessments are integrated into the routing recommendation system. The routing recommendation system takes into consideration both the popularity assessment and the length of the road. The best route recommended to the user minimizes the distance while including maximal tourism popularity. Experiments were conducted in two different scenarios. The empirical results show that the recommendation system is able to provide the user good travel planning including both top ranking landmarks and suitable routings in a city. Besides, the system offers user-generated semantic information for the recommended routes.