Neural ranking in information retrieval (IR) involves using deep learning models to rank search results based on their relevance to a user's query. Unlike traditional ranking models, which may rely on hand-crafted features, neural ranking models automatically learn to rank results by analyzing large datasets of queries and documents.
Neural ranking models typically use techniques such as pairwise ranking (comparing pairs of results) or listwise ranking (optimizing entire lists of results) to train the system. These models are often based on architectures like deep neural networks or transformers, which can capture complex relationships between queries and documents.
For example, a neural ranking model might learn to rank a set of news articles based on their relevance to a specific search query. The model could use embeddings or contextual features to understand the semantic meaning behind the query and the content of the articles, improving the accuracy of search rankings.