Transformer-Based Multi-Model Rank Fusion for Cross-Domain Recommendation and Dense Information Retrieval

Rank fusion is an effective approach for combining outputs from multiple ranking models, though existing methods often have difficulty generalizing across different data environments. This thesis introduces EmbMerge, a transformer-based framework that frames rank fusion as a sequence modeling task b