Modeling Dynamic Item Tendency Bias in Sequential Recommendation with Causal Intervention

Sequential recommendation is a critical but challenging task in capturing users' potential preferences due to inherent biases in the data. Existing debiasing recommendation methods aim to eliminate biases from historical interaction data collected by recommender systems and have shown promising results. However, there is another significant bias that hinders the improvement of sequential recommendation models: dynamic item tendency bias. This bias arises because a period might have some unique tendencies consisting of items interacted with by users with the same intent, leading to a dynamic tendency distribution that biases the model training towards these tendencies. To address this issue, we propose a causal approach to model dynamic item tendency bias in sequential recommendation. We first extract tendencies on carefully designed item-item graphs through community detection. We then use causal intervention to conduct deconfounded training to capture true user preferences and introduce the beneficial item tendency bias to the inference process through optimal transport techniques. Experimental results on four real-world datasets demonstrate that our proposed method consistently outperforms state-of-the-art debiasing recommendation methods, confirming that our model is effective in reducing dynamic item tendency bias and dealing with tendency drifts.