POI Recommendation Method of Neural Matrix Factorization Integrating Auxiliary Attribute Information

Xiaoyan Li, Shenghua Xu (), Tao Jiang, Yong Wang, Yu Ma and Yiming Liu
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Xiaoyan Li: School of Geomatics, Liaoning Technical University, Fuxin 123000, China
Shenghua Xu: Research Centre of Geo-Spatial Big Data Application, Chinese Academy of Surveying and Mapping, Beijing 100830, China
Tao Jiang: School of Resource and Environmental Sciences, Wuhan University, Wuhan 430072, China
Yong Wang: Research Centre of Geo-Spatial Big Data Application, Chinese Academy of Surveying and Mapping, Beijing 100830, China
Yu Ma: School of Geomatics, Liaoning Technical University, Fuxin 123000, China
Yiming Liu: School of Spatial Informatics and Geomatics Engineering, Anhui University of Science and Technology, Huainan 232000, China

Mathematics, 2022, vol. 10, issue 19, 1-14

Abstract: Point-of-interest (POI) recommendation is the prevalent personalized service in location-based social networks (LBSNs). A single use of matrix factorization (MF) or deep neural networks cannot effectively capture the complex structure of user–POI interactions. In addition, to alleviate the data-sparsity problem, current methods primarily introduce the auxiliary information of users and POIs. Auxiliary information is often judged to be equally valued, which will dissipate some of the valuable information. Hence, we propose a novel POI recommendation method fusing auxiliary attribute information based on the neural matrix factorization, integrating the convolutional neural network and attention mechanism (NueMF-CAA). First, the k -means and term frequency–inverse document frequency (TF-IDF) algorithms are used to mine the auxiliary attribute information of users and POIs from user check-in data to alleviate the data-sparsity problem. A convolutional neural network and an attention mechanism are applied to learn the expression of auxiliary attribute information and distinguish the importance of auxiliary attribute information, respectively. Then, the auxiliary attribute information feature vectors of users and POIs are concatenated with their respective latent feature vectors to obtain the complete latent feature vectors of users and POIs. Meanwhile, generalized matrix factorization (GMF) and multilayer perceptron (MLP) are used to learn the linear and nonlinear interactions between users and POIs, respectively, and the last hidden layer is connected to integrate the two parts to alleviate the implicit feedback problem and make the recommendation results more interpretable. Experiments on two real-world datasets, the Foursquare dataset and the Weibo dataset, demonstrate that the proposed method significantly improves the evaluation metrics—hit ratio (HR) and normalized discounted cumulative gain (NDCG).

Keywords: point-of-interest recommendation; neural matrix factorization; auxiliary attribute information; location-based social networks (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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