Journal Article

Relational POI recommendation model combined with geographic information

Tian, Zhihui, He, Xiaohui, Wei, Haitao, Li, Ke

2022

Overview

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.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Algorithms / Biology and Life Sciences / Cluster Analysis / Clustering / Collaboration / Data collection / Earth Sciences / Female / Geography / Geospatial data / Humans / Laboratories / Linear functions / Location based services / Location-based systems / Meteorology / Modelling / Physical Sciences / Primary Ovarian Insufficiency / R&D / Research & development / Research and Analysis Methods / Serogroup / Social aspects / Social networks / Social Sciences / Trigonometric functions / User behavior