Optimizing Sequence-Based POI Recommendations: From Sequence Adjustment to Transformer-XL Integration

Sequence-based Point-Of-Interest (POI) recommendations are increasingly crucial for location-based services and social platforms, offering nuanced insights into predicting user preferences from historical interaction patterns. However, a significant challenge arises from the non-uniform distribution of user-POI interaction sequences, where user preferences are often obscured by irregular and sporadic activities. This paper proposes an innovative Uniform Sequence Balancing (USB) strategy, addressing the critical issue of non-uniform sequences by utilizing the standard deviation of time intervals to achieve uniformity. Our approach transforms non-uniform sequences into uniform ones, thereby facilitating more accurate preference capture. We leverage the Transformer eXtra Long (Transformer-XL) model, known for its ability to discern long-term dependencies, and integrate it with our USB strategy to propose the Sequential Transformer-XL Recommender (STR). Our comprehensive experiments on two widely used public datasets demonstrate the effectiveness of STR, which significantly outperforms state-of-the-art models. The proposed STR not only optimizes recommendation performance but also paves the way for future research on sequence-based recommendation systems.