@Article{jihpp.2020.010211,
AUTHOR = {Ming Zhao, Tao Liu},
TITLE = {Improving POI Recommendation via Non-Convex Regularized Tensor  Completion},
JOURNAL = {Journal of Information Hiding and Privacy Protection},
VOLUME = {2},
YEAR = {2020},
NUMBER = {3},
PAGES = {125--134},
URL = {http://www.techscience.com/jihpp/v2n3/40851},
ISSN = {2637-4226},
ABSTRACT = {The problem of low accuracy of POI (Points of Interest)
recommendation in LBSN (Location-Based Social Networks) has not been 
effectively solved. In this paper, a POI recommendation algorithm based on nonconvex regularized tensor completion is proposed. The fourth-order tensor is 
constructed by using the current location category, the next location category, 
time and season, the regularizer is added to the objective function of tensor 
completion to prevent over-fitting and reduce the error of the model. The 
proximal algorithm is used to solve the objective function, and the adaptive 
momentum is introduced to improve the efficiency of the solution. The 
experimental results show that the algorithm can improve recommendation 
accuracy while reducing the time cost.},
DOI = {10.32604/jihpp.2020.010211}
}