Ranking Model

Ranking models aim to order items (e.g., documents, products, ads) based on relevance or preference, optimizing for accuracy and efficiency in information retrieval and recommendation systems. Current research emphasizes improving ranking accuracy through techniques like knowledge distillation, multimodal fusion, and the incorporation of large language models (LLMs) for both reranking and zero-shot ranking, often focusing on mitigating biases and computational costs. These advancements have significant implications for various applications, including search engines, e-commerce platforms, and question-answering systems, by enhancing user experience and improving the effectiveness of retrieval-augmented generation.