Sequential recommender systems have achieved state-of-the-art recommendation performance by modeling the sequential dynamics of user activities. However, in most recommendation scenarios, the popular items comprise the major part of the previous user actions. Therefore, the learned models are biased towards the popular items irrespective of the user's real interests. In this paper, we propose a structural causal model-based method to address the popularity bias issue for sequential recommendation model learning. For more generalizable modeling, we disentangle the popularity and interest representations at both the item side and user context side. Based on the disentangled representation, we identify a more effective structural causal graph for general recommendation applications. Then, we design delicate sequential models to apply the aforementioned causal graph to the sequential recommendation scenario for unbiased prediction with counterfactual reasoning. Furthermore, we conduct extensive offline experiments and online A/B tests to verify the proposed \textbf{DCR} (Disentangled Counterfactual Reasoning) method's superior overall performance and understand the effectiveness of the various introduced components. Based on our knowledge, this is the first structural causal model specifically designed for the popularity bias correction of sequential recommendation models, which achieves significant performance gains over the existing methods.

翻译：序列推荐系统通过建模用户行为的序列动态性，已取得最优的推荐性能。然而在大多数推荐场景中，流行物品构成了用户历史行为的主要部分。因此，学习模型会偏向流行物品，而忽略了用户的真实兴趣。本文提出一种基于结构因果模型的方法，以解决序列推荐模型学习中的流行度偏差问题。为实现更强的泛化建模，我们在物品侧和用户上下文侧分别解缠了流行度与兴趣表示。基于解缠后的表示，我们识别出更适合通用推荐应用的结构因果图。随后，我们设计了精密的序列模型，将上述因果图应用于序列推荐场景，通过反事实推理实现无偏预测。此外，我们开展了大量离线实验与在线A/B测试，验证所提出的DCR（解缠反事实推理）方法在整体性能上的优越性，并理解各引入组件的有效性。据我们所知，这是首个专为序列推荐模型流行度偏差校正设计的结构因果模型，相较于现有方法实现了显著的性能提升。