What is neural search?
Neural search refers to neural information retrieval using deep neural networks, machine learning, and natural language processing. It’s an advanced version of a search engine capable of understanding user intent behind a search query and matching it with the most accurate and relevant results.
What are the benefits of neural search?
Some of the benefits of neural search include:
- The capability to detect deeper context behind user queries;
- Understanding relationships between search data, matching results, and user interests;
- Searching for various types of data, including 3D information, videos, audio, images, written content, etc.;
- Reduced manual search engine maintenance;
- Streamlined search engine management;
- Empowering users to build recommendation and topic-specific information retrieval systems;
- Automatic adjustment to personalized search queries;
- Search result diversification.
What is the difference between neural search and vector search?
Though the two terms refer to related technologies, there’s a slight difference between neural and vector search. Vector search refers to a search technique aimed at detecting similar vector parameters in a given dataset.
Neural search, on the other hand, relies on vector search to improve the accuracy, relevancy, and performance of neural networks and deep learning-based information retrieval systems.
What is an example of a neural search?
The best example of a neural search is Amazon’s search engine called A9. It works like a recommendation engine that provides personalized product recommendations based on previous user activity, interests, and preferences.