Budgeted Embedding Table For Recommender Systems: A Novel Approach for Memory-Efficient Recommendations

The author proposes a novel Budgeted Embedding Table (BET) method to optimize embedding sizes for users and items efficiently, ensuring memory budgets are met. By leveraging set-based action formulation and fitness prediction networks, BET outperforms existing methods in real-world datasets.