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Article

Incorporating Memory-Based Preferences and Point-of-Interest Stickiness into Recommendations in Location-Based Social Networks

by 1, 1,* and 2
1
School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China
2
Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing 100083, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2021, 10(1), 36; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010036
Received: 13 October 2020 / Revised: 17 December 2020 / Accepted: 11 January 2021 / Published: 15 January 2021
(This article belongs to the Special Issue Geovisualization and Social Media)
In location-based social networks (LBSNs), point-of-interest (POI) recommendations facilitate access to information for people by recommending attractive locations they have not previously visited. Check-in data and various contextual factors are widely taken into consideration to obtain people’s preferences regarding POIs in existing POI recommendation methods. In psychological effect-based POI recommendations, the memory-based attenuation of people’s preferences with respect to POIs, e.g., the fact that more attention is paid to POIs that were checked in to recently than those visited earlier, is emphasized. However, the memory effect only reflects the changes in an individual’s check-in trajectory and cannot discover the important POIs that dominate their mobility patterns, which are related to the repeat-visit frequency of an individual at a POI. To solve this problem, in this paper, we developed a novel POI recommendation framework using people’s memory-based preferences and POI stickiness, named U-CF-Memory-Stickiness. First, we used the memory-based preference-attenuation mechanism to emphasize personal psychological effects and memory-based preference evolution in human mobility patterns. Second, we took the visiting frequency of POIs into consideration and introduced the concept of POI stickiness to identify the important POIs that reflect the stable interests of an individual with respect to their mobility behavior decisions. Lastly, we incorporated the influence of both memory-based preferences and POI stickiness into a user-based collaborative filtering framework to improve the performance of POI recommendations. The results of the experiments we conducted on a real LBSN dataset demonstrated that our method outperformed other methods. View Full-Text
Keywords: location-based social networks; point-of-interest recommendations; memory-based preferences; point-of-interest stickiness; collaborative filtering location-based social networks; point-of-interest recommendations; memory-based preferences; point-of-interest stickiness; collaborative filtering
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MDPI and ACS Style

Zhang, H.; Gan, M.; Sun, X. Incorporating Memory-Based Preferences and Point-of-Interest Stickiness into Recommendations in Location-Based Social Networks. ISPRS Int. J. Geo-Inf. 2021, 10, 36. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010036

AMA Style

Zhang H, Gan M, Sun X. Incorporating Memory-Based Preferences and Point-of-Interest Stickiness into Recommendations in Location-Based Social Networks. ISPRS International Journal of Geo-Information. 2021; 10(1):36. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010036

Chicago/Turabian Style

Zhang, Hang, Mingxin Gan, and Xi Sun. 2021. "Incorporating Memory-Based Preferences and Point-of-Interest Stickiness into Recommendations in Location-Based Social Networks" ISPRS International Journal of Geo-Information 10, no. 1: 36. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010036

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