An effective points of interest recommendation approach based on embedded meta‐path of spatiotemporal data. Issue 2 (21st September 2022)

• Record Type: Journal Article
• Authors: Song, Rui; Li, Tong; Dong, Xin; Ding, Zhiming
• Other Names: Yu Hui guestEditor.
• Is Part Of: Expert systems. Volume 40:Issue 2(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-09-21
• Subjects: meta‐path -- network embedding -- POI recommendation -- spatiotemporal data; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.13145
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: With the development of mobile networks and the rapid prevalence of location‐based social networks (LBSN), a massive volume of spatiotemporal data has been generated, which is valuable for points of interest (POI) recommendation. However, current studies have not unleashed the full power of such spatiotemporal data, which either explore only a single dimension of the data or consider multiple factors in an asynchronous fashion. In this article, we propose a novel spatiotemporal network‐based recommender framework (STNBR) to effectively recommend POIs for users. Specifically, we first establish a comprehensive conceptual model of spatiotemporal data, involving various essential factors for POIs recommendation. On top of the conceptual model, we design a series of meaningful meta‐paths that simultaneously consider the time and location factors to precisely capture the semantics of user behaviours. By profiling users based on their embedded meta‐paths, our approach can yield meaningful POIs recommendations. We have evaluated our proposal using a realistic dataset obtained from Foursquare and Gowalla, the results of which show that our STNBR model outperforms existing approaches.