An Attentional Recurrent Neural Network for Personalized Next Location Recommendation

Qing Guo, Zhu Sun, Jie Zhang, Yin-Leng Theng
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Most existing studies on next location recommendation propose to model the sequential regularity of check-in sequences, but suffer from the severe data sparsity issue where most locations have fewer than five following locations. To this end, we propose an Attentional Recurrent Neural Network (ARNN) to jointly model both the sequential regularity and transition regularities of similar locations (neighbors). In particular, we first design a meta-path based random walk over a novel knowledge
more » ... to discover location neighbors based on heterogeneous factors. A recurrent neural network is then adopted to model the sequential regularity by capturing various contexts that govern user mobility. Meanwhile, the transition regularities of the discovered neighbors are integrated via the attention mechanism, which seamlessly cooperates with the sequential regularity as a unified recurrent framework. Experimental results on multiple real-world datasets demonstrate that ARNN outperforms state-of-the-art methods.
doi:10.1609/aaai.v34i01.5337 fatcat:nfd3l2eu25bnncqc3fhlkcxmoy