Document Vector Ranking for Search Relevance
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Solution Overview
Problem
Current search engine systems face challenges in providing relevant search results within acceptable response times, as existing ranking algorithms struggle to effectively utilize proximity values between document vectors to enhance user satisfaction and document similarity determination.
Innovation Solution
A computer-implemented method and system that uses Machine Learning Algorithms, specifically Neural Networks, to generate document and query vectors, determining proximity values between them to rank documents based on relevance and similarity, thereby improving search result relevance and user satisfaction by supplementing missing document data and enriching the server's knowledge about documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional ranking algorithms are used to rank documents, then the system is simpler to implement, but the relevance of search results and user satisfaction deteriorate
Solution Approach 1:
The patent introduces document vectors as an intermediary representation between raw document data and ranking decisions. These vectors capture semantic meaning and enable sophisticated similarity calculations without requiring complex rule-based systems. The vector space model serves as a mediator that transforms textual data into a form amenable to mathematical operations for relevance assessment.
Solution Approach 2:
The patent transforms documents into vector representations with specific parameters (dimensions, magnitudes, directions) that enable quantitative comparison. By changing the representation parameters from discrete text tokens to continuous vector spaces, the system enables sophisticated similarity measurements using standard mathematical operations, improving relevance without proportionally increasing complexity.
2Reliability
If sophisticated ranking algorithms utilizing proximity values are implemented, then user satisfaction and document similarity determination improve, but the response time deteriorates
Solution Approach 1:
The patent pre-computes document vectors and stores them in advance, so that when a query is submitted, the system only needs to compute query vectors and calculate proximity values rather than performing full-text analysis. This preliminary transformation of documents into vector form significantly reduces the time required for similarity calculations during actual search operations.
Solution Approach 2:
The patent creates vector copies of documents that preserve semantic information in a compact numerical format. These vector representations serve as efficient proxies for the original documents, enabling rapid similarity calculations without repeatedly processing the full text content, thus reducing response time while maintaining accuracy.
3Loss of information
If proximity values between document vectors are utilized for ranking, then the knowledge base for ranking is enriched, but the computational resources required increase
Solution Approach 1:
The patent calculates proximity values between query vectors and document vectors, but does not necessarily compute all possible pairwise proximities in the entire document collection. The system can limit calculations to a subset of relevant documents or use approximate nearest neighbor techniques, obtaining sufficient ranking information without the excessive computational cost of complete pairwise comparisons.
Data Source
AI summary
A method and server for ranking a plurality of documents are disclosed. The method includes determining, by the server, for each document from the plurality of documents a proximity value between a respective document vector and an other document vector. A given proximity value for a respective document being indicative of a similarity between (i) the respective document and (ii) at least one other one from the plurality of documents. The method also comprises using, by the server, the proximity values of the plurality of documents for ranking the plurality of documents.