Hybrid search combines multiple search methods to improve the relevance and accuracy of search results. Typically, it integrates both traditional keyword-based search and more modern, context-aware methods like semantic search using machine learning models.
In hybrid search, the system might first filter results using traditional techniques like keyword matching (using Boolean operators or TF-IDF) and then apply machine learning models to rank or refine the results based on semantic relevance. For example, a traditional IR system may return a list of documents based on keyword frequency, but a semantic search model (such as BERT) can reorder the results based on deeper understanding of the query's meaning.
Hybrid search is effective for improving user experience in systems where both high precision (like exact term matching) and high recall (semantic meaning) are important. It is used in many modern search engines, content management systems, and e-commerce platforms to provide more relevant and personalized search results.