Multi Objective, Rankings, Selection Bias, Transformers, Ml So Good.
helps the model generalize better to different scenarios. Robustness to Position Variations: Neighboring bias can be more robust to variations in the position of items, as it focuses on the local context rather than relying solely on the fixed position defined by indices or page numbers. WRITER at