Learning Algorithm for Domain Name Suggestion
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Solution Overview
Problem
Current methods for suggesting domain names to users lack effectiveness in personalizing recommendations based on user behavior, often mixing unrelated search activities and failing to provide timely and relevant suggestions.
Innovation Solution
A domain name registrar collects and logs user activities to create training data, using a learning algorithm to suggest domain names by tokenizing searches, suggested names, and registered names, ensuring related activities are combined and used to train the algorithm to predict future domain name suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user activities are collected and stored in a training database to train a learning algorithm, then the relevance and accuracy of domain name suggestions is improved, but the device complexity and data processing requirements increase
Solution Approach 1:
The system segments user activities into distinct components (domain name searches, selected suggested domain names, registered domain names) and processes them separately through tokenization and filtering before training the learning algorithm. This segmentation allows manageable processing of complex user behavior data while maintaining accuracy.
Solution Approach 2:
The patent introduces a training database as an intermediary component that stores and organizes user activity data before it is used to train the learning algorithm. This intermediary structure simplifies the data flow and reduces the complexity of direct real-time processing.
2Manufacturing precision
If tokenization and filtering of user activities are performed to ensure related activities are combined, then the purity of training data is improved, but the processing time and computational resources increase
Solution Approach 1:
The system performs tokenization and filtering of user activities in advance during the data collection phase, storing the processed data in a training database before it is needed for model training. This preliminary processing eliminates the need for time-consuming data preparation during actual model training, reducing computational delays.
3Adaptability or versatility
If the learning algorithm is trained on user behavior data to predict future domain name suggestions, then the adaptability to user needs is improved, but the amount of training data required increases
Solution Approach 1:
The system extracts only the most relevant features from user activity data through tokenization, focusing on key elements such as domain name searches, selected suggestions, and registered domains. This extraction process reduces the volume of training data needed while maintaining the adaptability of the learning algorithm to user needs.
Data Source
AI summary
Methods are taught for creating training data for a learning algorithm, training the learning algorithm with the training data and using the trained learning algorithm to suggest domain names to users. A domain name registrar may store activities of a user on a registrar website. Preferably, domain name searches, selected suggested domain names and domain names registered to the user are stored as the training data in a training database. The training data may be stored so that earlier activities act as inputs to the learning algorithm while later activities are the expected outputs of the learning algorithm. Once trained, the learning algorithm may receive activities of other users and suggest domain names to the other users based on their activities.