Lightweight Next POI Recommendation Combining Long and Short-term Preferences

The popularity of intelligent mobile devices and location-based social networks have promoted research on point-of-interest (POI) recommendation. Most studies have used complex models to extract multi-dimensional user check-in features, which have improved recommendation performance but significantly increased parameter size and computational complexity. In this study, we propose a lightweight (LS-LNR) method for next POI recommendation to achieve the optimal balance between performance and computational cost. To improve the recommendation performance, user check-in behavior learning is divided into long-term and short-term patterns, and multi-dimensional user check-in sequence features are integrated in LS-LNR. To reduce computational complexity, only the feedforward layer in the transformer mechanism is used to extract user features. Moreover, we propose a lighter feedforward layer consisting of linear and convolutional layers, and adopt the GELU activation function. Extensive experiments are conducted on a large-scale real-life dataset collected from Gowalla, and the results demonstrate that LS-LNR has significantly improvements in recall and normalized discounted cumulative gain even with lower operational parameters compared with baseline approaches.