Landmark Seasonal Travel Distribution and Activity Prediction Based on Language-specific Analysis
Landmark Seasonal Travel Distribution and Activity Prediction Based on Language-specific Analysis
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DOI：
10.1109/bigdata.2018.8622103
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发表时间：
2018-12
期刊：
影响因子：
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通讯作者：
Siya Bao;M. Yanagisawa;N. Togawa
中科院分区：
文献类型：
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作者：
Siya Bao;M. Yanagisawa;N. Togawa
网络媒体社区已经跨越全球，并日益加快学术和工业领域智能旅游推荐系统的发展。然而，存在一个瓶颈，即忽略了用户在不同语言群体中的季节性旅行分布(何时访问)的差异。本文提出了一种基于用户评论的季节性活动预测算法，该算法在2012-2017年间使用不同的语言群。我们利用在线用户评论，提供每个地标的参观时间和详细的活动描述。通过在TripAdvisor上对三个大城市300个地标的417,787条用户评论的积累，我们分析了不同语言在旅游分布上的差异。之后，生成对每种语言组的未来旅行分布的预测。然后，通过对旺季和淡季的评论内容，区分每个地标的潜在旺季和淡季，并提取具有代表性的季节活动。在三个城市的实验结果表明，该算法在旺季检测和季节活动预测方面比以往的研究更准确。
Online media communities have globally spanned and have increasingly accelerated the development of intelligent travel recommendation systems in both academic and industrial fields. However, there is a bottleneck that differences in users’ seasonal travel distributions (when to visit) in various language groups are ignored. This paper proposes a seasonal activity prediction algorithm based on user comments over the period of 2012 to 2017 in different language groups. We take the advantage of online user comments which provide visiting time for each landmark and detailed activity description. With the accumulation of 417,787 user comments on TripAdvisor for 300 landmarks in three big cities, we analyze the language-specific differences in travel distributions. After that, prediction of future travel distribution for each language group is generated. Then potential peak and off seasons of each landmark are distinguished and representative seasonal activities are extracted through comment contents for peak and off seasons, respectively. Experimental results in the three cities show that the proposed algorithm is more accurate in terms of peak season detection and seasonal activity prediction than previous studies.