A Novel Next New Point-of-Interest Recommendation System based on Simulated User Travel Decision-Making

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Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/112683

Title:	A Novel Next New Point-of-Interest Recommendation System based on Simulated User Travel Decision-Making
Authors:	Xia, Yingyuan;Xiao, Yingyuan;Jiao, Xu;Jiao, Xu;Wenguang Zh;Wenguang Zheng;Wang, Hongya;Wang, Hongya;許慶賢;Hsu, Robert
Contributors:	資訊工程學系
Keywords:	Next new POI recommendationTensorLocation based social networksPreference
Date:	2019-09
Issue Date:	2020-08-19 07:12:26 (UTC+0)
Publisher:	亞洲大學
Abstract:	In recent years, with the rapid development of mobile Internet technology, positioning technology, wireless sensor technology and the popularity of smart phones, location-based social networks (LBSNs) as shown in Fig. 1 and its application services have developed rapidly. The currently popular LBSNs are Foursquare, Gowalla, Facebook Place, Microsoft GeoLife, Bikely, Flickr, Panomamio, etc. The LBSNs represented by Foursquare, Gowalla, and Facebook Place mainly provide check-in services for POIs. Encourage users to share their favorite POIs with friends in the form of check-in and share their experiences and tips for POIs. The main difference between LBSNs and Online Social Networks (OSN) is that LBSNs add geographical location information. Location data bridges the gap between the physical and digital worlds and enables a deeper understanding of users’ preferences and behaviors. Since users generate a large amount of check-in data in LBSNs, it is possible to recommend the unvisited POIs to users. POI recommendation can help users better understand their city and explore the surrounding environment. Therefore, POI recommendation is of high value to both users and the business owners of POIs.
Relation:	Future Generation Computer Systems-The International Journal of eScience
Appears in Collections:	[資訊工程學系] 期刊論文

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