Meta-learning for Few-shot Recommendation

Recommender systems are ubiquitous in modern digital platforms, guiding users to relevant items from vast catalogs. A significant challenge arises in few-shot recommendation scenarios, where new items or users have very limited interaction data, making it difficult for traditional recommendation models to provide accurate suggestions. This paper explores the application of meta-learning techniques to address the few-shot recommendation problem. We propose a meta-learning framework that learns to quickly adapt to new recommendation tasks with only a handful of examples. Our approach trains a meta-learner on a collection of diverse recommendation tasks, enabling it to acquire transferable knowledge about how to learn effectively from limited data. We investigate various meta-learning algorithms, including MAML and Reptile, and adapt them to the unique characteristics of recommendation tasks. Extensive experiments on several real-world datasets demonstrate that our meta-learning approach significantly outperforms strong baselines in few-shot recommendation settings, leading to more accurate and personalized recommendations for emerging items and users.