Graph databases are specialized databases designed to handle relationships between data entities through graph structures, with nodes representing entities and edges representing relationships. In information retrieval (IR), graph databases are used to model complex relationships between data points, enabling more advanced search and recommendation capabilities.
For example, in a recommendation system, a graph database can link users to products, reviews, or other users based on their preferences, enabling the generation of personalized recommendations. In IR, graph databases can improve search relevance by incorporating semantic relationships and context, such as connecting related terms, concepts, or documents.
Graph databases are especially useful for tasks like knowledge graph-based search, social network analysis, and entity resolution, where understanding and leveraging relationships between entities can significantly enhance search results. Popular graph databases include Neo4j and Amazon Neptune.