self-training

**Self-training** is **a semi-supervised approach where a model generates labels for unlabeled data and retrains on confident predictions** - Pseudo-labeled samples expand training coverage beyond labeled datasets. **What Is Self-training?** - **Definition**: A semi-supervised approach where a model generates labels for unlabeled data and retrains on confident predictions. - **Core Mechanism**: Pseudo-labeled samples expand training coverage beyond labeled datasets. - **Operational Scope**: It is used in recommendation and advanced training pipelines to improve ranking quality, label efficiency, and deployment reliability. - **Failure Modes**: Confirmation bias can reinforce early model mistakes if confidence thresholds are weak. **Why Self-training Matters** - **Model Quality**: Better training and ranking methods improve relevance, robustness, and generalization. - **Data Efficiency**: Semi-supervised and curriculum methods extract more value from limited labels. - **Risk Control**: Structured diagnostics reduce bias loops, instability, and error amplification. - **User Impact**: Improved recommendation quality increases trust, engagement, and long-term satisfaction. - **Scalable Operations**: Robust methods transfer more reliably across products, cohorts, and traffic conditions. **How It Is Used in Practice** - **Method Selection**: Choose techniques based on data sparsity, fairness goals, and latency constraints. - **Calibration**: Use conservative confidence filtering and periodic relabeling with validation-based rollback checks. - **Validation**: Track ranking metrics, calibration, robustness, and online-offline consistency over repeated evaluations. Self-training is **a high-value method for modern recommendation and advanced model-training systems** - It improves data efficiency when labeled data is limited.