TY - JOUR AU - Guo, Qing AU - Sun, Zhu AU - Zhang, Jie AU - Theng, Yin-Leng PY - 2020/04/03 Y2 - 2024/03/28 TI - An Attentional Recurrent Neural Network for Personalized Next Location Recommendation JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 34 IS - 01 SE - AAAI Technical Track: AI and the Web DO - 10.1609/aaai.v34i01.5337 UR - https://ojs.aaai.org/index.php/AAAI/article/view/5337 SP - 83-90 AB - <p>Most existing studies on next location recommendation propose to model the <em>sequential regularity</em> of check-in sequences, but suffer from the severe data sparsity issue where most locations have fewer than five <em>following locations</em>. To this end, we propose an <em>Attentional Recurrent Neural Network</em> (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 graph 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.</p> ER -