Embedding Based Retrieval

Embedding-based retrieval (EBR) aims to efficiently find relevant information within massive datasets by representing data points (e.g., documents, products) as dense vectors and using approximate nearest neighbor search. Current research emphasizes improving EBR's robustness and efficiency through techniques like multi-modal and multi-task learning, optimized training objectives (including self-supervised and contrastive learning), and efficient indexing methods such as binary embeddings and tree-based structures. These advancements are significantly impacting various applications, including recommendation systems, e-commerce search, and real-time information retrieval, leading to improved accuracy and scalability.