Neural IR differs from traditional IR in that it leverages deep learning models, particularly neural networks, to understand and process text data more effectively. While traditional IR systems primarily rely on keyword matching and statistical models (such as TF-IDF and BM25), neural IR systems focus on learning representations of both queries and documents in vector spaces, capturing more nuanced semantic meaning.
In neural IR, a query and a document are typically transformed into embeddings (dense vector representations) using models like word2vec, BERT, or other transformer-based models. These embeddings are then compared using similarity measures such as cosine similarity or dot product to determine relevance, whereas traditional IR systems rely on term frequency matching.
Neural IR allows for better handling of complex queries, synonyms, and semantic meaning, making it particularly suitable for applications like semantic search and recommendation systems. It also reduces reliance on explicit feature engineering, allowing the model to automatically learn relevant patterns from the data.