Method and system for personalized embedding search engine

Abstract

Methods and systems for optimized large-scale semantic search and information retrieval are disclosed. Via comparing similarity scores between the search query and the resources, the personalized embedding-based search engine and system can select probable answers with the consideration of the query's intent and context. The resources can comprise all relevant content or customized content related to the searched site or domain. The system can apply custom score adjustments to the similarity scores. In addition, the system can partition and process a resource in a number of different manners to generate different resource vectors. Further, the search query can be an explicit query or an implied query that is inferred from the user's actions.