Next Article in Journal
Geographic Information Metadata—An Outlook from the International Standardization Perspective
Previous Article in Journal
Evaluation of Topological Consistency in CityGML
Article Menu
Issue 6 (June) cover image

Export Article

Open AccessArticle

Discovering Memory-Based Preferences for POI Recommendation in Location-Based Social Networks

1,* and 2
1
Donlinks School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China
2
Meituan-Dianping Co., Ltd., Beijing, 100102, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(6), 279; https://doi.org/10.3390/ijgi8060279
Received: 19 April 2019 / Revised: 7 June 2019 / Accepted: 9 June 2019 / Published: 14 June 2019
  |  
PDF [2830 KB, uploaded 19 June 2019]
  |  

Abstract

Point-of-interest (POI) recommendations in location-based social networks (LBSNs) allow online users to discover various POIs for social activities occurring in the near future close to their current locations. Research has verified that people’s preferences regarding POIs are significantly affected by various internal and external contextual factors, which are therefore worth extensive study for POI recommendation. However, although psychological effects have also been demonstrated to be significantly correlated with an individual’s preferences, such effects have been largely ignored in previous studies on POI recommendation. For this paper, inspired by the famous memory theory in psychology, we were interested in whether memory-based preferences could be derived from users’ check-in data. Furthermore, we investigated how to incorporate these memory-based preferences into an effective POI recommendation scheme. Consequently, we refer to Ebbinghaus’s theory on memory, which describes the attenuation of an individual’s memory in the form of a forgetting curve over time. We first created a memory-based POI preference attenuation model and then adopted it to evaluate individuals’ check-ins. Next, we employed the memory-based values of check-ins to calculate the POI preference similarity between users in an LBSN. Finally, based on this memory-based preference similarity, we developed a novel POI recommendation method. We experimentally evaluated the proposed method on a real LBSN data set crawled from Foursquare. The results demonstrate that our method, which incorporates the proposed memory-based preference similarity for POI recommendation, significantly outperforms other methods. In addition, we found the best value of the parameter H in the memory-based preference model that optimizes the recommendation performance. This value of H implies that an individual’s memory usually has an effect on their daily travel choices for approximately 300 days. View Full-Text
Keywords: location-based social networks; point-of-interest recommendation; memory-based preference; Ebbinghaus forgetting curve; collaborative filtering location-based social networks; point-of-interest recommendation; memory-based preference; Ebbinghaus forgetting curve; collaborative filtering
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Gan, M.; Gao, L. Discovering Memory-Based Preferences for POI Recommendation in Location-Based Social Networks. ISPRS Int. J. Geo-Inf. 2019, 8, 279.

Show more citation formats Show less citations formats

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

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top