Open Access

ARTICLE

Improving POI Recommendation via Non-Convex Regularized Tensor Completion

Ming Zhao^*, Tao Liu

School of Computer Science and Engineering, Central South University, Changsha, 410000, China

* Corresponding Author: Ming Zhao. Email:

Journal of Information Hiding and Privacy Protection 2020, 2(3), 125-134. https://doi.org/10.32604/jihpp.2020.010211

Received 11 July 2020; Accepted 21 August 2020; Issue published 18 December 2020

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.

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