Dynamic User Cluster Personalization via Self-Learning Feature Generation
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Solution Overview
Problem
Existing personalization systems in commercial recommendation systems rely on pre-defined feature sets, which cannot dynamically generate new features or adapt quickly to changing user behavior, requiring users to undergo a learning process before they can benefit from personalization.
Innovation Solution
A computing system and method that acquires user information, including language data, to create usage logs, generate user features, determine clustering features, form user clusters, and apply personalized content based on these features, allowing for dynamic updates and adaptation to user behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If pre-defined feature sets are used for personalization, then system complexity is reduced and ease of manufacture is improved, but adaptability to changing user behavior deteriorates and new features cannot be generated dynamically
Solution Approach 1:
The patent implements dynamic feature generation by continuously learning from user interactions and automatically creating new features without manual intervention. The system transitions from static pre-defined features to dynamic features that evolve with user behavior, resolving the contradiction between ease of implementation and adaptability.
Solution Approach 2:
The system performs self-learning and self-updating by automatically generating new features from user interaction data without requiring manual feature engineering. This self-service capability enables the system to adapt to changing user behavior while maintaining ease of operation.
2Device complexity
If pre-defined clustering processes are used, then device complexity is reduced, but the system cannot dynamically update based on rapid-changing user language behavior
Solution Approach 1:
The clustering system dynamically updates by continuously learning from new user interactions and automatically adjusting cluster assignments. The system incorporates real-time language behavior changes into cluster definitions without requiring manual reconfiguration, maintaining low complexity while achieving high adaptability.
Solution Approach 2:
The system uses feedback from user interactions to continuously refine clustering. User language behavior data is fed back into the clustering process, enabling automatic updates that adapt to changing patterns while keeping the system architecture simple.
3Measurement precision
If users go through a learning process before benefiting from personalization, then personalization accuracy is improved, but loss of time increases and productivity decreases
Solution Approach 1:
The system performs preliminary learning in the background without requiring user involvement. By pre-processing and learning from aggregate user data before individual personalization is needed, the system achieves accurate personalization immediately when applied to users, eliminating their learning curve.
Solution Approach 2:
The system performs self-learning from user interactions without requiring users to actively learn or configure settings. The automatic learning process happens in the background, providing immediate personalization benefits to users while they continue using the system naturally.
Data Source
AI summary
A system and method for dynamic cluster personalization is provided. A method of dynamic cluster personalization comprises acquiring information from a user, creating a usage log based on the acquired user information including language information and generating user features based on the usage log. The method further comprises determining a clustering feature from the user features, creating a user cluster based on the clustering feature, determining a personalization feature within the user cluster from the user features, generating a personalization for the user cluster based on the personalization feature and applying the personalization to the users in the user cluster.