# Meta-Learning with Adaptive Weighted Loss for Imbalanced Cold-Start Recommendation

## TL;DR

This paper introduces a meta-learning framework with adaptive weighted loss to improve cold-start sequential recommendation by addressing imbalanced user feedback, demonstrating superior performance on real-world datasets.

## Contribution

It is the first to incorporate adaptive loss for imbalanced ratings in meta-learning-based cold-start recommendation, enhancing personalization accuracy.

## Key findings

- Improved recommendation accuracy on real-world datasets.
- Effective handling of imbalanced user feedback.
- Outperforms existing meta-learning approaches.

## Abstract

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.