Open AccessPOI Recommendation System using Hypergraph Embedding and Logical Matrix Factorization
Author(s) -
Yangyang Li,
Wang Ya-jun,
Zhang Mi-yuan
Publication year - 2022
Publication title -
journal of artificial intelligence and copsule networks
Language(s) - English
Resource type - Journals
ISSN - 2582-2012
DOI - 10.36548/jaicn.2022.1.003
Subject(s) - hypergraph , point of interest , matrix decomposition , computer science , recommender system , embedding , factorization , data mining , logical data model , information retrieval , precision and recall , matrix (chemical analysis) , theoretical computer science , algorithm , artificial intelligence , data modeling , mathematics , database , discrete mathematics , eigenvalues and eigenvectors , physics , materials science , quantum mechanics , composite material
Aiming at the problems of inaccurate recommendation and single consideration in the traditional Points of Interest (POI) recommendation model, a POI Recommendation System using Hypergraph Embedding and Logical Matrix Factorization (HE-LMF) has been proposed. The user's check-in points of interest and time information are sampled by hypergraph embedding technology, and users with similar points of interest to the target user are found, and their points of interest are recommended to the target user. At the same time, through the geographic recommendation model based on logical matrix decomposition, the regions with many user check-in times and the correlation of each region are considered. The results of the two models are weighted, and top-k is selected to recommend to the user. Finally, experiments are carried out on the two datasets of gowalla and foursquare, and compared with the three models USG, PFMMGM and LRT. The experimental results show that the HE-LMF algorithm can effectively improve the accuracy and recall rate of POI recommendation.