Sequential recommenders have made great strides in capturing a user's preferences. Nevertheless, the cold-start recommendation remains a fundamental challenge in which only a few user-item interactions are available for personalization. Gradient-based meta-learning approaches have recently 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. However, while meta-learning algorithms generally assume that task-wise samples are evenly distributed over classes or values, user-item interactions are not that way in real-world applications (e.g., watching favorite videos multiple times, leaving only good ratings and no bad ones). As a result, in the real-world, imbalanced user feedback that accounts for most task training data may dominate the user adaptation 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 imbalance of each user's rating distribution and accordingly computes adaptive loss for user-specific learning. It is the first work to tackle the impact of imbalanced ratings in cold-start sequential recommendation scenarios. We design adaptive weighted loss and improve the existing meta-learning algorithms for state-of-the-art sequential recommendation methods. Extensive experiments conducted on real-world datasets 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显著进展。然而，冷启动推荐仍是一项基本挑战，仅少数用户-项目交互可用于个性化。基于梯度的元学习方法因其快速适应和易于集成的能力，最近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需要适应的任务。然而，尽管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，但在现实应用中，用户-项目交互并非如此（例如，多次观看喜爱的视频、仅给出好评而无差评）。因此，在现实场景中，占多数任务训练数据的非均衡用户反馈可能主导用户适应过程，阻碍元学习算法学习有意义的元知识以实现个性化推荐。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，该框架捕捉每个用户评分分布的非均衡性，并据此计算自适应损失以进行用户特定学习。这是首个解决冷启动序列推荐场景中非均衡评分影响的工作。我们设计了自适应加权损失，改进了现有元学习算法，以用于最先进的序列推荐方法。在真实数据集上进行的大量实验证明了我们框架的有效性。