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A personalized POI route recommendation system based on heterogeneous tourism data and sequential pattern mining

机译：基于异构旅游数据和顺序模式挖掘的个性化POI路线推荐系统

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摘要

Planning a personalized POI route before touring a new city is an important travel preparation activity; however, it is a challenging and time-consuming task for tourists. Although some previous works focus on suggesting POI visit list or sequences, they fail to suggest personalized POI routes due to ignoring multifaceted tourism contexts. Also, they often suffer from tourist cold start or data sparsity problem because of the lack of tourism related data. To address the above weaknesses, we first propose a novel method to integrate heterogeneous tourism data collected from websites to construct a POI knowledgebase and massive structured POI visit sequences. Next, a POI-Visit sequential pattern mining algorithm is proposed to generate various fine-grained candidate POI routes from POI visit sequences while considering various tourism contexts. At the POI route recommendation stage, our system retrieve and rank a list of candidate routes according to the querying tourist's tourism contexts, including the intended travel duration, the companion type in trip, the visit season and the preferring POI tourism types, etc. In our validation experiments, we select Guilin city in China as an example to construct a real POI knowledgebase which consists of 132 POIs and 8778 POI traffic time, and construct 5694 structured POI visit sequences based on 10,109 downloaded original travelogues. The experimental results demonstrate the advantages of our system in recommending fine-grained and high personalized POI routes for specific tourists.
机译：在游览新城市之前，规划个性化的POI路线是一项重要的旅行准备活动；然而，对于游客来说，这是一项艰巨而耗时的任务。尽管先前的一些工作着重于建议POI访问列表或序列，但由于忽略了多方面的旅游环境，因此未能提出个性化的POI路线。此外，由于缺乏与旅游相关的数据，他们经常遭受游客冷门或数据稀疏性的困扰。为了解决上述缺点，我们首先提出一种新颖的方法来整合从网站收集的异构旅游数据，以构建一个POI知识库和大规模的结构化POI访问序列。接下来，提出了一种POI-Visit顺序模式挖掘算法，该算法从POI访问序列中生成各种细粒度的候选POI路线，同时考虑各种旅游环境。在POI路线推荐阶段，我们的系统会根据查询的旅游者的旅游环境来检索候选路线列表并对其进行排名，包括预期的旅行持续时间，旅途中的同伴类型，访问季节和POI偏好的旅游类型等。在验证实验中，我们以中国桂林市为例，构建了一个包含132个POI和8778个POI交通时间的真实POI知识库，并基于10109条下载的原始旅行记录构建了5694个结构化POI访问序列。实验结果证明了我们的系统在为特定游客推荐细粒度和高度个性化POI路线方面的优势。

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