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Recommending prime spots of a destination and time to visit from geo-tagged social data

机译:从带有地理标签的社交数据中推荐目的地的主要景点和参观时间

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Planning a trip can be a tedious task. One has to search for what places to visit at a destination (i.e. area) and what time to visit the destination. Sometimes this can be a time-consuming task because there are too much information available, and it is hard for one to choose which information to trust. In this paper we present a recommendation system clustering geo-tagged social data in a destination from each information source - Flickr and Foursquare - and combining the results from these diverse information sources to recommend places to visit. Our experimental results show that our recommendation system automatically suggests prime spots in Yellowstone national park with 0.83 precision and 0.927 NDCG, and in Yosemite national park with 0.8 precision and 0.912 NDCG. In addition, visualizing temporal information of social data helps travelers to decide when to visit a destination.
机译:计划行程可能是一项繁琐的任务。人们必须搜索目的地(即区域)上要参观的地方以及什么时间去目的地。有时这可能是一项耗时的任务,因为可用的信息太多,而且很难选择要信任的信息。在本文中,我们提出了一种推荐系统,该系统将来自每个信息源(Flickr和Foursquare)的目的地中带有地理标签的社交数据聚类,并将来自这些不同信息源的结果组合起来,以推荐参观地点。我们的实验结果表明,我们的推荐系统会自动以0.83精度和0.927 NDCG的精度推荐黄石国家公园和以0.8精度和0.912 NDCG的优胜美地国家公园的景点。另外,可视化社交数据的时间信息有助于旅行者决定何时访问目的地。

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