Next POI Recommendation via Graph Embedding Representation From H-Deepwalk on Hybrid Network

Abstract

With the rapid development of location-based social networks (LBSNs), point of interest (POI) recommendation has become more and more popular personalized service. Cold start problem and poor interpretability are two main challenges of the traditional recommendation system. In this paper, we propose a novel social and sequence-aware next POI recommendation model. Our model utilizes an improved DeepWalk on heterogeneous network to learn better POI representation which contains social relationship and geographical influence. In this way, the cold start problem and interpretability problem can be solved to some extend. As for the user preference learning, we use LSTM (Long Short-Term Memory) mechanism to infer user interest, taking both long term and short term intentions into consideration. The model is trained in a metric learning framework. The experimental results on two real LBSN datasets show our model has outstanding performance in terms of AUC, Recall, and ACC. Furthermore, we can obtain 20% performance improvement on spare dataset which indicates that our model can well solve the cold start problem.

Keywords

• LBSN
• next-POI recommendation
• LSTM
• DeepWalk