POI types characterization based on geographic feature embeddings

IF 0.4 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Salatiel Dantas Silva, C. E. Campelo, Maxwell Guimarães De Oliveira
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引用次数: 0

Abstract

Representing Points of Interest (POI) types, such as restaurants and shopping malls, is crucial to develop computational mechanisms that may assist in tasks such as urban planning and POI recommendation. The POI co-occurrences in different spatial regions have been used to represent POI types in high-dimensional vectors. However, such representations do not consider the geographic features (e.g. streets, buildings, rivers, parks) in the vicinity of POIs which may contribute to characterize such types. In this context, we propose the Geographic Context to Vector (GeoContext2Vec), an approach that relies on geographic features in the POIs' vicinity to generate POI types representation based on embeddings. We carried out an experiment to evaluate the GeoContext2Vec by using a POI type representation from the state-of-the-art that it does not consider geographic features. The promising results show that the geographic information provided by the GeoContext2Vec outperforms the state-of-the-art and demonstrates the relevance of surrouding geographic features on representing POI type more precisely.
基于地理特征嵌入的POI类型表征
表示兴趣点(POI)类型,如餐馆和购物中心,对于开发可能有助于城市规划和POI推荐等任务的计算机制至关重要。利用不同空间区域的POI共现来表示高维向量上的POI类型。然而,这种表述没有考虑到poi附近的地理特征(例如街道、建筑物、河流、公园),这些特征可能有助于描述这类类型。在这种情况下,我们提出了地理上下文到向量(GeoContext2Vec),这是一种依赖于POI附近的地理特征来基于嵌入生成POI类型表示的方法。我们进行了一项实验,通过使用不考虑地理特征的最先进的POI类型表示来评估GeoContext2Vec。结果表明,GeoContext2Vec提供的地理信息优于目前最先进的地理信息,并证明了周围地理特征与更精确地表示POI类型的相关性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Applied Computing Review
Applied Computing Review COMPUTER SCIENCE, INFORMATION SYSTEMS-
自引率
40.00%
发文量
8
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