Causality-driven User Modeling for Sequential Recommendations over Time

Contemporary sequential recommendation systems predominantly leverage statistical correlations derived from user interaction histories to predict future preferences. However, these correlations often mask implicit challenges. On the one hand, user data is frequently plagued by implicit, noisy feedback, misdirecting users towards items that fail to align with their actual interests, which is magnified in sequential recommendation contexts. On the other hand, prevalent methods tend to over-rely on similarity-based attention mechanisms across item pairs, which are prone to utilizing heuristic shortcuts, thereby leading to suboptimal recommendation.