Next Article in Journal
Spatial Analysis of Settlement Structures to Identify Pattern Formation Mechanisms in Inter-Urban Systems
Previous Article in Journal
Regional Terrain Complexity Assessment Based on Principal Component Analysis and Geographic Information System: A Case of Jiangxi Province, China

STS: Spatial–Temporal–Semantic Personalized Location Recommendation

School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
School of Software, Tsinghua University, Beijing 100085, China
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(9), 538;
Received: 7 August 2020 / Revised: 31 August 2020 / Accepted: 7 September 2020 / Published: 8 September 2020
The rapidly growing location-based social network (LBSN) has become a promising platform for studying users’ mobility patterns. Many online applications can be built based on such studies, among which, recommending locations is of particular interest. Previous studies have shown the importance of spatial and temporal influences on location recommendation; however, most existing approaches build a universal spatial–temporal model for all users despite the fact that users always demonstrate heterogeneous check-in behavior patterns. In order to realize truly personalized location recommendations, we propose a Gaussian process based model for each user to systematically and non-linearly combine temporal and spatial information to predict the user’s displacement from their currently checked-in location to the next one. The locations whose distances to the user’s current checked-in location are the closest to the predicted displacement are recommended. We also propose an enhancement to take into account category information of locations for semantic-aware recommendation. A unified recommendation framework called spatial–temporal–semantic (STS) is introduced to combine displacement prediction and the semantic-aware enhancement to provide final top-N recommendation. Extensive experiments over real datasets show that the proposed STS framework significantly outperforms the state-of-the-art location recommendation models in terms of precision and mean reciprocal rank (MRR). View Full-Text
Keywords: location recommendation; Gaussian process; spatial–temporal–semantic; location-based social networks; top-N recommendation location recommendation; Gaussian process; spatial–temporal–semantic; location-based social networks; top-N recommendation
Show Figures

Figure 1

MDPI and ACS Style

Li, W.; Liu, X.; Yan, C.; Ding, G.; Sun, Y.; Zhang, J. STS: Spatial–Temporal–Semantic Personalized Location Recommendation. ISPRS Int. J. Geo-Inf. 2020, 9, 538.

AMA Style

Li W, Liu X, Yan C, Ding G, Sun Y, Zhang J. STS: Spatial–Temporal–Semantic Personalized Location Recommendation. ISPRS International Journal of Geo-Information. 2020; 9(9):538.

Chicago/Turabian Style

Li, Wenchao, Xin Liu, Chenggang Yan, Guiguang Ding, Yaoqi Sun, and Jiyong Zhang. 2020. "STS: Spatial–Temporal–Semantic Personalized Location Recommendation" ISPRS International Journal of Geo-Information 9, no. 9: 538.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop