ADPR: An Attention-based Deep Learning Point-of-Interest Recommendation Framework

Abstract

With the development of location-based social networks (LBSNs), Point-of-Interest (POI) recommendation has attracted lots of attention. Most of the existing studies focus on recommending POIs to users based on their recent check-ins. However, the recent check-ins may contain some daily check-ins that users are not really interested in. If a model treats the recent check-ins equally, it is non-trivial to capture the actual preference of users. To address the issue of mining the actual preferences of users in the POI recommendation, we propose an attention-based deep learning POI recommendation framework (ADPR), which consists of a latent representation method and an attention-based deep convolutional neural network. To learn the embedding of users and POIs, we propose a latent representation method, which incorporates the geographical influence and the categories of POIs to capture the relationships between POIs better. Further, we propose an attention-based deep convolutional neural network, which employs the attention mechanism to filter the important information in the recent check-ins, to recommend POIs to users based on the latent representations of users and the recent check-ins. We conduct experiments on a real-world LBSN dataset to evaluate our framework, and the experimental results show the effectiveness of our framework.