Explainable uncertainty attribution for sequential recommendation

Sequential recommendation systems suggest products based on users’ historical behaviours. The inherent sparsity of user-item interactions in a vast product space often leads to unreliable recommendations. Recent research addresses this challenge by leveraging auxiliary product relations to mitigate recommendation uncertainty, and quantifying uncertainty in recommendation scores to modify the candidates selection. However, such approaches may not be efficient due to the requirement of additional side information or providing suboptimal recommendations. To enhance sequential recommendation performance by leveraging uncertainty information, we introduce Explainable Uncertainty Attribution (ExUA). We employ gradient-based saliency attribution to identify sources of uncertainty stemming from sequential interactions. Experimental findings on Amazon and MovieLens datasets demonstrate ExUA’s effectiveness in identifying interactions that induce uncertainty, resulting in a 6%+ improvement in NDCG@20 scores when the un-certainty information is integrated into a post-hoc training phase.