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MDP-based Itinerary Recommendation using Geo-Tagged Social Media

机译：使用带有地理标签的社交媒体的基于MDP的路线推荐

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摘要

Planning vacations is a complex decision problem. Many variables like the place(s) to visit, how many days to stay, the duration at each location, and the overall travel budget need to be controlled and arranged by the user. Automatically recommending travel itineraries would thus be a remedy to quickly converge to an individual trip that is tailored to a user's interests. While on a trip, users frequently share their experiences on social media platforms e.g., by uploading photos of specific locations and times of day. Their uploaded data serves as an asset when it comes to gathering information on their journey. In this paper, we leverage social media, more explicitly photo uploads and their tags, to reverse engineer historic user itineraries. Our solution grounds on Markov decision processes that capture the sequential nature of itineraries. The tags attached to the photos provide the factors to generate possible configurations and prove crucial for contextualising the proposed approach. Empirically, we observe that the predicted itineraries are more accurate than standard path planning algorithms.
机译：计划假期是一个复杂的决策问题。用户需要控制和安排许多变量，例如访问的地点，停留的天数，每个位置的持续时间以及总体旅行预算。因此，自动推荐旅行路线将是快速收敛到针对用户兴趣量身定制的个人旅行的一种补救措施。在旅途中，用户经常在社交媒体平台上分享他们的经验，例如，通过上传特定位置和一天中的时间的照片。当他们收集旅途信息时，他们上传的数据将成为一种资产。在本文中，我们利用社交媒体（更明确地说是照片上传及其标签）来反向设计历史用户路线。我们的解决方案基于马尔科夫决策流程，该流程可捕获路线的顺序性质。附在照片上的标签为生成可能的配置提供了因素，并证明对于拟议方法的环境至关重要。从经验上讲，我们观察到的预测路线比标准路径规划算法更准确。

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