Recent advances in correlation-based sequential recommendation systems have demonstrated substantial success. Specifically, the attention-based model outperforms other RNN-based and Markov chains-based models by capturing both short- and long-term dependencies more effectively. However, solely focusing on item co-occurrences overlooks the underlying motivations behind user behaviors, leading to spurious correlations and potentially inaccurate recommendations. To address this limitation, we present a novel framework that integrates causal attention for sequential recommendation, CausalRec. It incorporates a causal discovery block and a CausalBooster. The causal discovery block learns the causal graph in user behavior sequences, and we provide a theory to guarantee the identifiability of the learned causal graph. The CausalBooster utilizes the discovered causal graph to refine the attention mechanism, prioritizing behaviors with causal significance. Experimental evaluations on real-world datasets indicate that CausalRec outperforms several state-of-the-art methods, with average improvements of 7.21% in Hit Rate (HR) and 8.65% in Normalized Discounted Cumulative Gain (NDCG). To the best of our knowledge, this is the first model to incorporate causality through the attention mechanism in sequential recommendation, demonstrating the value of causality in generating more accurate and reliable recommendations.

翻译：基于相关性的序列推荐系统的最新进展已展现出显著成功。具体而言，注意力模型通过更有效地捕捉短期和长期依赖关系，其性能超越了其他基于RNN和马尔可夫链的模型。然而，仅关注物品共现会忽略用户行为背后的深层动机，导致虚假相关性和潜在的不准确推荐。为应对这一局限，我们提出了一种集成因果注意力机制用于序列推荐的新框架——CausalRec。该框架包含一个因果发现模块和一个因果增强器（CausalBooster）。因果发现模块学习用户行为序列中的因果图，我们提供了理论以保证所学因果图的可识别性。因果增强器利用发现的因果图来优化注意力机制，优先考虑具有因果重要性的行为。在真实数据集上的实验评估表明，CausalRec在多项指标上优于多种最先进方法，其中命中率（HR）平均提升7.21%，归一化折损累计增益（NDCG）平均提升8.65%。据我们所知，这是首个通过注意力机制在序列推荐中融入因果关系的模型，证明了因果关系在生成更准确、更可靠推荐方面的价值。