  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.
