Training Models on Sparse Data and Labels in Recommendation Systems: Overcoming Supervision and Feedback Limitations

Abstract

Large-scale recommendation systems consistently grapple with extreme data and label sparsity, where the vast majority of potential user-item interactions remain unobserved and unlabeled. This fundamental constraint severely limits model training, introducing biased gradients, degrading personalization quality, and amplifying popularity bias toward frequently interacted items while neglecting long-tail content. The challenge intensifies as platforms scale to serve diverse user populations across massive item catalogs, where traditional supervised learning approaches prove inadequate. This article provides a comprehensive examination of contemporary strategies designed to train robust recommendation models under severe supervision constraints. The article encompasses self-supervised learning techniques that leverage unlabeled interaction data through contrastive and pretext objectives, implicit feedback modeling approaches that extract weak supervision from behavioral signals while mitigating inherent biases, and transfer learning frameworks that incorporate knowledge from auxiliary tasks and cross-domain sources. Critical analysis reveals the practical trade-offs between sample efficiency, computational scalability, and recommendation fidelity across different sparsity regimes. By synthesizing theoretical foundations with real-world deployment considerations, this work offers actionable guidance for researchers and practitioners seeking to improve recommendation accuracy, enhance cold-start robustness, and expand long-tail coverage in production environments where explicit feedback remains scarce yet personalization expectations continue rising. The article bridges academic innovation with industrial implementation realities facing modern recommendation systems.