<ul><li>A new research study explores sustainable techniques to improve the data quality for training image-based explanatory models for Recommender Systems.</li><li>Current approaches in this domain often suffer from limitations due to sparse and noisy training data.</li><li>To address this, the study introduces three novel strategies for training data quality enhancement, including reliable negative training example selection, transform-based data augmentation, and text-to-image generative-based data augmentation.</li><li>Integration of these strategies in explainability models resulted in a 5% performance increase without compromising long-term sustainability.</li></ul>

Sustainable techniques to improve Data Quality for training image-based explanatory models for Recommender Systems

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