Sequential Recommendation Based on Causal Inference

Sequential recommender systems, representing an advanced approach in modeling user behavior sequences, excel in capturing the dynamic evolution of user interests. However, challenges such as data sparsity, noise, and bias significantly impair the accuracy of these recommendations. This study introduces a novel counterfactual sequence generation algorithm, anchored on pivotal items. It innovates by navigating the counterfactual domain of user behavior sequences, employing a strategy to simulate alternative product selection pathways. This approach generates a variety of counterfactual sequences, thereby enriching the dataset for model training. Additionally, we present a counterfactual generation mechanism that delineates the scope of counterfactual exploration, enhancing sequence plausibility through strategic key item delineation and the incorporation of stochastic perturbations. Addressing sequence generation challenges, we propose a contrastive learning framework designed to discern and preserve the “invariance” within user interests, thus bolstering model resilience. To further refine the algorithm, we incorporate principles of alignment and uniformity within the counterfactual domain, reinforcing the algorithm's precision and overall performance. Empirical validation across four public datasets corroborates the algorithm's effectiveness, demonstrating notable advancements in recommendation accuracy and robustness.