Aggregate Neural Semantic Network for Search Relevance
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Solution Overview
Problem
Conventional search engines struggle to provide highly relevant search results due to subjective user preferences and the presence of irrelevant information, as they rely solely on search queries and content analysis without considering user-specific semantic connections.
Innovation Solution
The implementation of an Aggregate Neural Semantic Network that processes search results by storing and updating semantic connections between keywords based on user preferences, utilizing a multi-layer neural network to select relevant hits and refine search outputs by incorporating context and meaning from previous user queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional search engines rely solely on search queries and content analysis, then the search process remains simple and fast, but the relevance of search results deteriorates due to subjective user preferences and irrelevant information
Solution Approach 1:
The patent segments the search processing into two distinct components: a conventional search engine that handles basic query matching and content analysis, and a neural network component that handles semantic understanding and user preference analysis. This segmentation allows each component to specialize in its strength while maintaining overall system efficiency.
Solution Approach 2:
The neural network acts as an intermediary between the conventional search engine and the user. It receives search queries and results from the conventional engine, processes them through learned semantic relationships and user preferences, and returns refined results. This intermediary layer adds intelligence without replacing the entire search system.
2Reliability
If search engines return comprehensive hits based on keyword matching, then the quantity of results increases, but the quality deteriorates due to inclusion of irrelevant information
Solution Approach 1:
The patent changes the parameter of result selection from simple keyword matching to semantic similarity scoring. The neural network computes similarity scores based on learned semantic relationships and user preferences, then filters results based on these scores. This parameter change maintains comprehensive coverage while improving quality by prioritizing relevant results.
Solution Approach 2:
The system performs partial action by not returning all matching results, but only those that meet a certain relevance threshold determined by the neural network. This selective filtering reduces the quantity of results while maintaining or improving quality, avoiding the need to process and display every possible hit.
3Adaptability or versatility
If search engines use subjective user preferences to improve result relevance, then the personalization increases, but the complexity of determining appropriate scores deteriorates
Solution Approach 1:
The neural network performs preliminary action by pre-learning semantic relationships and user preferences during training phases, before actual search queries are processed. This pre-computation of knowledge allows the system to quickly apply learned patterns to new queries without performing complex real-time analysis, reducing operational complexity.
Solution Approach 2:
The system uses self-service by automatically learning and updating user preferences through interaction data without requiring manual configuration or complex real-time analysis. The neural network adapts to user behavior patterns autonomously, simplifying the scoring determination process while maintaining high adaptability.
Data Source
AI summary
A system, method and computer program product for implementation of a Aggregate Neural Semantic Network, which stores the relationships and semantic connections between the key search words for each user. The Aggregate Neural Semantic Network processes the search results produced by a standard search engine such as, for example, Google or Yahoo!. The set of hits produced by the standard search engine is processed by the Aggregate Neural Semantic Network, which selects the hits that are relevant to a particular user based on the previous search queries made by the user. The Aggregate Neural Semantic Network can also use the connections between the terms (i.e., key words) that are most frequently used by all of the previous Aggregate Neural Semantic Network users. The Aggregate Neural Semantic Network is constantly updating and self-teaching. The more user queries are processed by the Aggregate Neural Semantic Network, the more comprehensive processing of search engine outputs is provided by the Aggregate Neural Semantic Network to the subsequent user queries.