RexRerankers: SOTA Rankers for Product Discovery and AI Assistants

RexRerankers are advanced reranking models designed to enhance e-commerce product relevance by accurately assessing product-query matches. The initiative introduces Amazebay, a comprehensive dataset aimed at refining product relevance models, and ERESS, a scoring suite for evaluating product discovery rerankers. The methodology involves a dual-phase training process that embraces annotation noise as a signal, enhancing model robustness and calibration. RexRerankers leverage a two-level deduplication process for data curation and employ a distributional-pointwise loss in training to better handle relevance ambiguity. Generative and classification-style rerankers were developed, with the RexReranker-0.6B achieving state-of-the-art nDCG metrics, effectively balancing performance with computational efficiency. The models are evaluated using nDCG for their ability to reward accurate ordering and graded relevance, addressing real-world challenges in e-commerce search, such as attribute mismatches and intent ambiguity.