Personalized Domain Name Search System Using Machine Learning
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Solution Overview
Problem
Existing domain name search systems lack personalization, failing to provide users with relevant domain name suggestions based on their preferences, behavior, and context.
Innovation Solution
A method utilizing machine learning models to generate seed domain names, second-level domains, and top-level domains based on user requests and profile information, followed by ranking and filtering these suggestions in real-time to provide personalized domain name recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If domain name search systems provide generic suggestions without personalization, then system complexity remains low, but user satisfaction and relevance of suggestions deteriorate
Solution Approach 1:
The system performs preliminary actions by collecting and storing user profile information, search history, and behavior data before domain name searches are conducted. This pre-processing of user data enables personalized suggestions without adding complexity to the actual search operation, as the personalization logic is already prepared in advance through profile aggregation and machine learning model training.
Solution Approach 2:
The patent introduces machine learning models as intermediaries between user input and domain name suggestions. These models process user profile information and search queries to generate personalized recommendations, acting as a mediator that translates complex user behavior patterns into relevant domain suggestions without requiring direct complex rule-based logic in the search system itself.
2Measurement precision
If domain name suggestions are generated without considering user behavior data, then processing time remains short, but relevance and personalization of suggestions worsen
Solution Approach 1:
User profile information, including search history and behavior data, is aggregated and processed in advance before domain name suggestions are needed. This preliminary preparation of user data allows the system to quickly generate relevant suggestions by querying pre-processed profiles rather than analyzing raw behavior data in real-time, thus maintaining low processing time while achieving high suggestion relevance.
Solution Approach 2:
The system changes parameters by using cached and aggregated user profile data with specific time window aggregations (e.g., recent search history weighted more heavily than older data). This parameter transformation converts raw behavioral data into condensed profile attributes that can be quickly processed by machine learning models to generate relevant suggestions without requiring extensive real-time computation.
3Measurement precision
If user profile information is not updated in real-time, then system complexity remains low, but personalization accuracy deteriorates
Solution Approach 1:
The system implements feedback mechanisms where user interactions with domain suggestions and registration behaviors are continuously monitored and fed back into the profile aggregation process. This feedback loop updates user profiles with new behavior data, allowing the system to adapt and improve personalization accuracy over time while managing complexity through structured data collection and model retraining processes.
Solution Approach 2:
User profile information is aggregated and cached in advance with structured data organization that facilitates efficient updates. By preparing the data infrastructure beforehand with proper schemas and caching strategies, the system can update profiles in real-time without creating excessive complexity, as the update processes follow pre-established data pipelines and aggregation rules.
Data Source
AI summary
A method includes receiving a request for a first domain from a requester with an identification, fetching requester profile using the identification, generating a seed domain based on the request and the requester profile, generating a plurality of SLDs based on the seed domain and the requester profile, generating a plurality of TLDs based on the seed domain and the requester profile, generating a first plurality of candidate domains based on the plurality of SLDs and the plurality of TLDs, generating a second plurality of candidate domains that are currently available by checking the first plurality of domains with a domain registrar, ranking the second plurality of candidate domains to be provided to the requester for a selection, receiving a selected domain, automatically updating the requester profile based on the request and the selection, and fetching the updated profile for generating another seed domain for a subsequent request.