Personalized Embedding Search Engine for Semantic Query Understanding
Solution Overview
Problem
Traditional search engines face difficulties in accurately understanding the semantic meaning of search queries, leading to suboptimal results in relevance and context, especially when handling user intent and experience-specific queries.
Innovation Solution
A personalized embedding-based search engine system that captures semantic meaning by generating similarity scores between user queries and resources, applying custom score adjustments based on user-specific factors and query attributes, and utilizing multiple sentence transformers to vectorize queries and resources for improved relevance ranking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional keyword-based search methods are used, then the search engine can operate with simple processing, but it fails to accurately understand the semantic meaning of search queries
Solution Approach 1:
The patent replaces traditional keyword-matching mechanical search methods with embedding-based vector representation and similarity computation. Search queries and resources are transformed into vector embeddings, and semantic similarity is calculated through vector operations, enabling the system to understand semantic meaning rather than just matching keywords.
Solution Approach 2:
The patent changes the fundamental parameters of search by transforming textual data into vector space representations. Instead of operating on discrete keywords, the system operates on continuous vector embeddings with semantic information, fundamentally changing how search relevance is measured and computed.
2Adaptability or versatility
If the system applies custom score adjustments based on user-specific factors, then the search results become more personalized and relevant, but the processing complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-computing and storing embedding vectors for all resources in the database before actual search queries are processed. This allows the system to quickly retrieve and compare vectors during search operations without performing complex computations in real-time, reducing processing complexity while maintaining personalization capabilities.
3Measurement precision
If multiple sentence transformers are used to vectorize queries and resources, then the semantic representation accuracy improves, but the computational time increases
Solution Approach 1:
The patent pre-computes and stores embedding vectors for all resources in the database before actual search queries are processed. This allows the system to quickly retrieve and compare vectors during search operations without performing complex embedding computations in real-time, significantly reducing computational processing time while maintaining high semantic representation accuracy.
Solution Approach 2:
The patent segments the computational process into offline pre-computation of resource embeddings and online real-time query processing. By separating these phases, the system can perform intensive computations offline and only execute lightweight similarity comparisons during actual user queries, reducing perceived computational time.
Data Source
AI summary
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.