  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.
