Sequential recommendation aims to learn users' dynamic preferences from their historical interactions and predict the next item they are most likely to engage with. In real-world scenarios, time-varying factors (e.g., product promotions, seasonal changes) induce distribution shifts in user interactions. Despite the demonstrated success of existing models, their generalization capability remains limited under such dynamic conditions. Current methods tackle this challenge by leveraging distributionally robust optimization (DRO) to optimize the "worst-case" loss or by employing manually designed data augmentation to enrich the training distribution. Despite their effectiveness, DRO-based approaches are inherently constrained by the sparsity of training data, limiting the range of distributions they can model, while manually designed augmentations risk introducing noise or irrelevant information that could distort user preference learning. Furthermore, these methods often overlook the sensitivity of user interactions to distribution shifts, which is essential for capturing the stable factors in the evolution of user preferences in real-world settings.
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Mitigating Distribution Shifts in Sequential Recommendation: An Invariance Perspective
OpenAlex · Recommender Systems and Techniques · 2025
Abstract
Sequential recommendation aims to learn users' dynamic preferences from their historical interactions and predict the next item they are most likely to engage with. In real-world scenarios, time-varying factors (e.g., product promotions, seasonal changes) induce distribution shifts in user interactions. Despite the demonstrated success of existing models, their generalization capability remains limited under such dynamic conditions. Current methods tackle this challenge by leveraging distributionally robust optimization (DRO) to optimize the "worst-case" loss or by employing manually designed data augmentation to enrich the training distribution. Despite their effectiveness, DRO-based approaches are inherently constrained by the sparsity of training data, limiting the range of distributions they can model, while manually designed augmentations risk introducing noise or irrelevant information that could distort user preference learning. Furthermore, these methods often overlook the sensitivity of user interactions to distribution shifts, which is essential for capturing the stable factors in the evolution of user preferences in real-world settings. In this work, we tackle the distribution shifting problem from the perspective of invariant learning. We propose a novel framework called Invariant Learning for Distribution Shifts in SEquential RecommendAtion (IDEA) to develop robust sequential recommendation. The key of IDEA lies on learning stable preferences across various distribution-aware environments. Since explicit environments are unavailable, we first extract multiple subsequences by dropping potential noise items, then extend environments with our proposed subsequence mixup. Given the simulated environments, IDEA then learns stable user preferences through invariant risk minimization (IRM) across various environments. To encourage the diversity of simulated environments, IDEA employs an adversarial training strategy to explore potential diverse environments, and further enhance the model's generalization to unseen test distributions. It is worth mentioning that IDEA is a flexible model-agnostic framework, which is applicable to various sequential recommendation models. Extensive experimental results on three public datasets clearly demonstrate the effectiveness of the proposed framework. Our code is available at: https://github.com/hermione314/IDEA.