Relational POI recommendation model combined with geographic information

Ke Li; Haitao Wei; Xiaohui He; Zhihui Tian

doi:10.1371/journal.pone.0266340

Relational POI recommendation model combined with geographic information

PLoS One. 2022 Apr 15;17(4):e0266340. doi: 10.1371/journal.pone.0266340. eCollection 2022.

Authors

Ke Li^{1

2}, Haitao Wei^{1

2}, Xiaohui He^{1

2}, Zhihui Tian^{1

2}

Affiliations

¹ The School of the Geo-Science & Technology, Zhengzhou University, Zhengzhou, Henan, China.
² Joint Laboratory of Eco-Meteorology, Zhengzhou University, Chinese Academy of Meteorological Sciences, Zhengzhou, Henan, China.

Abstract

Point of interest (POI) recommendation is a popular personalized location-based service. This paper proposes a Geographic Personal Matrix Factorization (GPMF) model that makes effective use of geographic information from the perspective of the relationship between POIs and users. This model considers the role of geographic information from multiple perspectives based on the locational relationship among users, the distributional relationship between users and POIs, and the proximity and clustering relationship among POIs. The GPMF mines the influence of geographic information on different objects and carries out unique modeling through cosine similarity, non-linear function, and k nearest neighbor (KNN). This study explored the influence of geographic information on POI recommendation through extensive experiments with data from Foursquare. The result shows that GPMF performs better than the commonly used POI recommendation algorithm in terms of both precision and recall. Geographic information through proximity relations effectively improves the recommendation algorithm.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms*
Cluster Analysis
Female
Humans
Primary Ovarian Insufficiency*
Serogroup

Grants and funding

X.H was received the National Key Research and Development Program of China under grant (number 2018YFB0505000); URL:https://service.most.gov.cn/ The funder support this word in study design and data collection and analysis. Z.T was received the Key Research and Development and Promotion Special Projects of Henan Province (Science and Technology Tackling Key Problems) under grant (number 192102210124). URL:http://kjt.henan.gov.cn/ The funder support this word in study design and data collection and analysis.