Free-Text Domain Recommendation with Knowledge-Based Personalization
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Solution Overview
Problem
Users struggle to find available domain names that are relevant to their business or website, as desired domain names are often already registered, and existing solutions fail to provide personalized and efficient domain name recommendations.
Innovation Solution
A system that receives user input and file content, tokenizes and tags keywords, removes irrelevant terms, and generates domain names using a knowledge base to suggest personalized and memorable domain names based on relevant concepts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users manually search for domain names, then they can find available options, but it consumes excessive time and effort
Solution Approach 1:
The system automatically generates domain name recommendations by analyzing user-provided keywords and business information without requiring manual searching. The domain name recommendation engine autonomously processes input data, matches it against the knowledge base, and produces personalized domain suggestions, enabling the system to serve itself in the domain name discovery process.
Solution Approach 2:
The system pre-processes user input by extracting keywords and concepts before domain name generation. By performing preliminary analysis of the input text and pre-matching against the knowledge base structure, the system prepares the data in advance, significantly reducing the time required for actual domain name recommendation generation.
2Productivity
If existing domain name generators are used, then domain names can be generated quickly, but they lack personalization and relevance to user business
Solution Approach 1:
The knowledge base acts as an intermediary between generic domain generation algorithms and user-specific requirements. It contains pre-organized industry terminology, business concepts, and domain patterns that mediate the transformation of user input into personalized domain recommendations, enabling both speed and relevance.
Solution Approach 2:
The system applies different processing strategies to different parts of the input based on their relevance. Keywords with higher relevance weights receive more sophisticated processing including synonym expansion and concept matching, while less critical terms receive standard processing, enabling personalized results without uniform computational overhead.
3Measurement precision
If comprehensive keyword analysis is performed, then relevant domain names are identified, but the process becomes computationally complex
Solution Approach 1:
The analysis process is divided into distinct segments: keyword extraction, knowledge base matching, relevance scoring, and domain generation. Each segment handles a specific aspect of the analysis independently, reducing overall complexity while maintaining comprehensive analysis through modular processing stages.
Data Source
AI summary
Systems and methods of the present invention provide for one or more server computers communicatively coupled to a network and configured to: receive a character string (e.g., a user input or a file content) from a client; match file tokens tokenized from the character string with knowledge base tokens in a database; generate labels/tags for the file tokens according to labels assigned to the knowledge base tokens and a second level domain (SLD) including a token from the file tokens; remove any tokens from the SLD flagged for removal in the database; generate a top level domain (TLD) and one or more domain names combining the SLD and the TLD; score each of one or more generated domain names according to domain name characteristics; and display a list of scored domain names on the client.