An Attentional Recurrent Neural Network for Personalized Next Location Recommendation

• DOI: 10.1609/aaai.v34i01.5337
• Publication Date: 2020-06-05T08:17:03Z
• Authors: Qing Guo; Zhu Sun; Jie Zhang; Yin-Leng Theng
• Keywords: 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology

Most existing studies on next location recommendation propose to model the sequential regularity of check-in sequences, but suffer from severe data sparsity issue where most locations have fewer than five following locations. To this end, we an Attentional Recurrent Neural Network (ARNN) jointly both and transition regularities similar (neighbors). In particular, first design a meta-path based random walk over novel knowledge graph discover neighbors heterogeneous factors. A recurrent neural network is then adopted by capturing various contexts that govern user mobility. Meanwhile, discovered are integrated via attention mechanism, which seamlessly cooperates with as unified framework. Experimental results multiple real-world datasets demonstrate ARNN outperforms state-of-the-art methods.