Guibing Guo

Next location prediction aims to infer the next location users are likely to visit based on their historical check-in data. However, existing methods assume that check-in data is complete, overlooking the subjective nature of users’ check-in behavior, leading to inaccurate capture of user prefere...

Next point-of-interest (POI) recommendation aims to predict users' next destinations from their mobility trajectories. Nevertheless, accurately capturing user preferences from check-in data remains challenging, as user behaviors are shaped by complex contextual factors and varying visit intention...

Sub-sequence splitting (SSS) has been demonstrated as an effective approach to mitigate data sparsity in sequential recommendation (SR) by splitting a raw user interaction sequence into multiple sub-sequences. Previous studies have demonstrated its ability to enhance the performance of SR models...

Sequential Recommendation (SR) learns user preferences from their historical interaction sequences and provides personalized suggestions. In real-world scenarios, most items exhibit sparse interactions, known as the tail-item problem. This issue limits the model's ability to accurately capture it...