Revisiting Long- and Short-Term Preference Learning for Next POI Recommendation With Hierarchical LSTM

Point-of-interest (POI) recommendation has drawn much attention with the widespread popularity of location-based social networks (LBSNs). Previous works define long- and short-term trajectories via long short-term memory (LSTM) to capture user's stable and current preference, and incorporate context factors to improve recommendation effectiveness. However, these factors have different impacts on POI recommendation, and meanwhile, they are mutually influenced. Existing studies either model all the factors separately, or feed them into the same LSTM model, which are less meticulous for modeling the LBSNs trajectories. To address such issues, we revisit the long- and short-term preference learning for next POI recommendation by presenting a novel framework that can model both POI level and semantic level check-in trajectories. We develop a hierarchical LSTM to learn the two-level representations and consider the interplay of the two-level features by adding factors to the gates of LSTMs for each trajectory. We further construct a semantic filter to improve the recommendation efficacy. Experimental results using two real-world check-in datasets indicate that the proposed framework outperforms four state-of-the-art baselines regarding two commonly used metrics.