TSRS: Trip Service Recommended System Based on Summarized Co-location Patterns

被引：4

作者：

Yang, Peizhong ^{[1
]}

Zhang, Tao ^{[1
]}

Wang, Lizhen ^{[1
]}

机构：

[1] Yunnan Univ, Sch Informat Sci & Engn, Kunming 650091, Yunnan, Peoples R China

来源：

WEB AND BIG DATA (APWEB-WAIM 2018), PT I | 2018年 / 10987卷

基金：

中国国家自然科学基金;

关键词：

Spatial data mining; Co-location pattern; Summarized pattern; Service recommendation;

D O I：

10.1007/978-3-319-96890-2_37

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Co-location patterns, whose instances are frequently located together, are particularly valuable for many applications. With co-location patterns, the location-based service recommendation can be made to give guidance to the user's trip. However, the number of co-location patterns is typically huge, thus it is restricted for practical applications. Based on summarized co-location patterns, we design a trip service recommended system, named TSRS. In TSRS, a large number of co-location patterns are compressed into a small quantity of summarized co-location patterns and their instances are stored into the retrieval tree for fast querying. Furthermore, TSRS provides the service point recommendation according to summarized co-location patterns, and route planning is given to help the user get to service points conveniently.

引用

页码：451 / 455

页数：5

共 6 条

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