Machine Learning Model for Personalized Domain Name Ranking
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Solution Overview
Problem
Current domain name registration processes lack personalization, resulting in irrelevant domain name suggestions for users, leading to decreased registration rates among registrars.
Innovation Solution
A method and system that utilize machine learning techniques to rank domain names based on user-specific features, such as gender, hobbies, and prior purchasing behavior, by generating a model that predicts user interest and presents personalized domain name suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If domain name suggestions are generated using traditional methods, then the process is simple and fast, but the suggestions are not personalized and relevant to user preferences
Solution Approach 1:
The system performs preliminary actions by collecting user information and training the machine learning model in advance. User profiles are built beforehand with demographic, behavioral, and preference data, and the model is pre-trained on historical domain registration data. This allows the system to quickly generate personalized suggestions without complex real-time processing when users actually search for domain names.
Solution Approach 2:
A machine learning model serves as an intermediary between raw user data and domain name suggestions. The model processes user profiles and historical data to generate personalized recommendations, acting as a mediator that translates complex user characteristics into tailored domain name suggestions without requiring direct complex rule-based processing.
2Measurement precision
If machine learning model is used to rank domain names, then the relevance of suggestions increases, but the processing time and computational resources increase
Solution Approach 1:
The machine learning model is trained in advance on historical domain registration data and user behavior patterns. Feature engineering and model training are performed beforehand, so that when users search for domain names, the system can quickly apply the pre-trained model to generate personalized rankings without extensive real-time computation.
Solution Approach 2:
The system focuses on processing only the most relevant features for each user query rather than analyzing all possible data points. By selecting and processing only the key features that most strongly predict domain name suitability, the system achieves high accuracy while reducing computational overhead and processing time.
3Adaptability or versatility
If comprehensive user information is collected for personalization, then the quality of recommendations improves, but user privacy concerns and data security requirements increase
Solution Approach 1:
The system extracts only the essential and necessary user features needed for domain name recommendations, such as browsing behavior, registration history, and basic demographic information. By selecting only the most relevant features and excluding unnecessary personal data, the system maintains recommendation quality while minimizing privacy risks and security requirements.
Solution Approach 2:
Users can control what information they share with the system and adjust their privacy settings. The system allows users to opt-in to data collection and provides transparency about how their information is used, enabling users to manage their own privacy while still receiving personalized recommendations based on the data they are comfortable sharing.
Data Source
AI summary
Disclosed are techniques for ranking domain names for presentation to a user. The techniques include obtaining, over a computer network, domain name data including, for each of a plurality of training domain names, respective user information; generating, by at least one electronic processor, a model relating at least features of each of the plurality of training domain names to respective user features derived from the respective user information; obtaining novel user information for a novel user; obtaining a plurality of domain names; ranking the plurality of domain names, using the model and novel input data including novel user features derived from the novel user information, according to predicted domain name suitability for the novel user; and providing a ranked list of the plurality of domain names.