@article{Chen2020, 
author = {Ming Chen and Wen-Zhong Li and Lin Qian and Sang-Lu Lu and Dao-Xu Chen},
title = {Next POI Recommendation Based on Location Interest Mining with Recurrent Neural Networks},
year = {2020},
journal = {Journal of Computer Science and Technology},
volume = {35},
number = {3},
pages = {603-616},
keywords = {point-of-interest (POI) recommendation, location-based service, location interest, mobile social network},
url = {https://www.sciopen.com/article/10.1007/s11390-020-9107-3},
doi = {10.1007/s11390-020-9107-3},
abstract = {In mobile social networks, next point-of-interest (POI) recommendation is a very important function that can provide personalized location-based services for mobile users. In this paper, we propose a recurrent neural network (RNN)-based next POI recommendation approach that considers both the location interests of similar users and contextual information (such as time, current location, and friends’ preferences). We develop a spatial-temporal topic model to describe users’ location interest, based on which we form comprehensive feature representations of user interests and contextual information. We propose a supervised RNN learning prediction model for next POI recommendation. Experiments based on real-world dataset verify the accuracy and efficiency of the proposed approach, and achieve best F1-score of 0.196 754 on the Gowalla dataset and 0.354 592 on the Brightkite dataset.}
}