Sequential recommenders have made great strides in capturing a user's preferences. Nevertheless, the cold-start recommendation remains a fundamental challenge as they typically involve limited user-item interactions for personalization. Recently, gradient-based meta-learning approaches have emerged in the sequential recommendation field due to their fast adaptation and easy-to-integrate abilities. The meta-learning algorithms formulate the cold-start recommendation as a few-shot learning problem, where each user is represented as a task to be adapted. While meta-learning algorithms generally assume that task-wise samples are evenly distributed over classes or values, user-item interactions in real-world applications do not conform to such a distribution (e.g., watching favorite videos multiple times, leaving only positive ratings without any negative ones). Consequently, imbalanced user feedback, which accounts for the majority of task training data, may dominate the user adaptation process and prevent meta-learning algorithms from learning meaningful meta-knowledge for personalized recommendations. To alleviate this limitation, we propose a novel sequential recommendation framework based on gradient-based meta-learning that captures the imbalanced rating distribution of each user and computes adaptive loss for user-specific learning. Our work is the first to tackle the impact of imbalanced ratings in cold-start sequential recommendation scenarios. Through extensive experiments conducted on real-world datasets, we demonstrate the effectiveness of our framework.

翻译：序列推荐器在捕捉用户偏好方面取得了长足进步。然而，冷启动推荐仍是一个根本性挑战，因为其通常涉及有限的用户-物品交互以实现个性化。近年来，基于梯度的元学习方法因其快速适应和易于集成的能力，在序列推荐领域崭露头角。元学习算法将冷启动推荐构建为小样本学习问题，其中每个用户被表示为一个待适应的任务。虽然元学习算法通常假设任务级样本在类别或数值上均匀分布，但现实应用中的用户-物品交互并不符合此类分布（例如，多次观看喜爱的视频，仅留下正面评分而无负面评分）。因此，占据任务训练数据主体的不平衡用户反馈可能主导用户适应过程，并阻碍元学习算法为个性化推荐学习有意义的元知识。为缓解这一局限，我们提出了一种基于梯度元学习的新型序列推荐框架，该框架可捕捉每个用户的不平衡评分分布，并为用户特定学习计算自适应损失。我们的工作首次解决了冷启动序列推荐场景中不平衡评分的影响。通过在真实数据集上开展广泛实验，我们验证了本框架的有效性。