Neural Network Enterprise Search Ranking
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Solution Overview
Problem
Current search engines face challenges in accurately ranking documents in enterprise environments due to differences in document characteristics and metadata availability compared to internet searches, leading to suboptimal user satisfaction metrics.
Innovation Solution
Implementing a neural network-based relevance ranking system that applies transformations to ranking features, such as Click Distance and URL Depth, and utilizes metadata and file type as predictors, with the ability to configure constants for specific environments, to provide a non-linear combination of inputs for improved ranking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a linear combination of ranking features is used, then the algorithm is simple and easy to implement, but the ranking precision is insufficient for enterprise environments
Solution Approach 1:
The patent replaces the traditional linear combination algorithm (mechanical system) with a neural network model. The neural network learns non-linear relationships between ranking features and document relevance automatically, substituting the manual feature weighting approach with an adaptive learning system that achieves superior ranking precision in enterprise environments.
Solution Approach 2:
The patent transforms the fixed linear weighting parameters into dynamic parameters that the neural network learns from training data. By changing from static weight coefficients to adaptive neural network weights and biases, the system can adjust to enterprise-specific document characteristics and metadata patterns, significantly improving ranking precision.
2Adaptability or versatility
If internet search features are used for enterprise search, then the system can leverage existing search capabilities, but the ranking quality is suboptimal due to different document characteristics
Solution Approach 1:
The patent applies local quality by tailoring the neural network ranking model specifically for enterprise environments. Instead of using a generic internet search model, the system learns enterprise-specific patterns from local data characteristics, metadata structures, and user behavior within the organization, achieving both adaptability and high precision simultaneously.
Solution Approach 2:
The patent implements preliminary action by training the neural network model on enterprise-specific data before deployment. The system performs offline training to learn organization-specific document characteristics and relevance patterns, then applies this pre-learned knowledge to improve online ranking performance without requiring real-time adaptation during actual search operations.
Data Source
AI summary
A neural network is used to process a set of ranking features in order to determine the relevancy ranking for a set of documents or other items. The neural network calculates a predicted relevancy score for each document and the documents can then be ordered by that score. Alternate embodiments apply a set of data transformations to the ranking features before they are input to the neural network. Training can be used to adapt both the neural network and certain of the data transformations to target environments.