POI recommendation model based on Attention-based Gated Recurrent Unit Network

Abstract

Point-of-Interest (POI) recommendation is one of the main functions in Location-based Social Networks (LBSNs). At present, some POI recommendation models couldn't effectively capture users' complex information and their recommendation performances are not good enough. Motivated by this, we propose a kind of POI recommendation model which is based on Attention-based Gated Recurrent Unit Network (Att-GRU). Firstly, we adopt the Gated Recurrent Unit (GRU) network to learn the complex sequential transition patterns from users' check-in behavior. Secondly, we extract user's preferences by using the target-guided attention mechanism. Finally, we conduct extensive experiments on a real-world LBSN dataset, and the experimental results demonstrate that our proposed Att-GRU based POI recommendation model has better performance than those of other mainstream ones in some evaluation criteria.