Residual Spatio-Temporal Collaborative Networks for Next POI Recommendation

As location-based services become increasingly integrated into users' lives, the next point-of-interest (POI) recommendation has become a prominent area of research. Currently, many studies are based on Recurrent Neural Networks (RNNs) to model user behavioral dependencies, thereby capturing user interests in POIs. However, these methods lack consideration of discrete check-in information, failing to comprehend the complex motivations behind user behavior. Moreover, the information collaboration efficiency of existing methods is relatively low, making it challenging to effectively incorporate the numerous collaborative signals within the historical trajectory sequences, thus limiting improvements in recommendation performance. To address the issues mentioned above, we propose a novel Residual Spatio-Temporal Collaborative Network (RSTCN) for improved next POI recommendation. Specifically, we design an encoder-decoder architecture based on residual linear layers to better integrate spatio-temporal collaborative signals by feature projection at each time step, thus improving the capture of users' long-term dependencies. Furthermore, we have devised a skip-learning algorithm to construct discrete data in a skipping manner, aiming to consider potential relationships between discrete check-ins and thus enhance the modeling capacity of short-term user dependencies. Extensive experiments on two real-world datasets demonstrate that our model significantly outperforms state-of-the-art methods.