The article explores the evolution of web-scale information retrieval from traditional keyword matching to advanced embedding-based techniques. Keyword matching often misinterprets user intent, overlooks synonyms, and struggles with typos, thus limiting the relevance of search results. To overcome these limitations, embedding-based approaches use deep learning to generate semantic vectors that enhance retrieval performance and accuracy. However, challenges remain, especially concerning the efficiency of approximate nearest neighbor (ANN) algorithms applied to vast web data, indicating an ongoing need for innovation in retrieval methods and algorithms.
Traditional keyword matching in information retrieval fails to understand user intent, which leads to irrelevant results and limits the diversity of responses, requiring query alterations to be effective.
Advancements in embedding-based retrieval have transformed web-scale information retrieval by leveraging deep learning to produce semantic embedding vectors, which improve the accuracy and relevance of search results.
Collection
[
|
...
]