Sequential recommendation aims to predict users’ next interactions by analyzing historical behavioral data. Traditional methods typically focus on learning fine-grained feature representations or extracting high-level user preferences to enhance recommendation accuracy. However, they often overlook the dynamic nature of user demand, which can shift over short periods and may resemble random noise. In our previous work, we introduced CoDeR, a framework that captures demand shifts and mitigates confounding biases through backdoor adjustment. Despite its effectiveness, CoDeR has limitations in its causal relation modeling, particularly in neglecting the role of user interest as a confounder. In this work, we propose CoDeR+, an enhanced framework that refines key components of CoDeR. First, we extend the original User Demand Extraction module into Interest-aware User Demand Modeling, introducing two submodules that explicitly model user interest and integrate it into demand representations. Second, we introduce a new Robust Counterfactual Demand Reasoning module, where user interest is treated as an additional confounder alongside demand drift, improving the causal correction process. Additionally, we provide a rigorous theoretical analysis of the updated backdoor adjustment and propose a simplified probability estimation method that reduces computational complexity. Extensive experiments on four real-world datasets demonstrate the effectiveness of CoDeR+. The source code for both CoDeR and CoDeR+ is publicly available at https://github.com/hellolst23/CoDeR .

Sequential recommendation systems aim to predict the next item based on users' historical interactions. While traditional methods focus on learning feature representations or user preferences, they often struggle with detecting subtle demand shifts in short sequences, especially when these shifts are obscured by noise or biases. To address these issues, we propose CoDeR (Counterfactual Demand Reasoning), a novel framework designed to handle demand shifts in sequential recommendations with greater precision. CoDeR features two key modules: (1) the User Demand Extraction module, which utilizes self-attention mechanisms and demand graphs to identify and model demand shifts from minimal user interactions; and (2) the Counterfactual Demand Reasoning module, which employs causal effect analysis and backdoor adjustment techniques to distinguish true demand shifts from noisy or biased signals. Our approach represents the first application of counterfactual reasoning to sequential recommendation systems. Comprehensive experiments on three real-world datasets demonstrate that CoDeR significantly outperforms existing baselines.