Content Personalization Metrics Using Cross-User Hash Analysis
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Solution Overview
Problem
Existing speech processing systems struggle to quantify and adjust the level of personalization in content output to users, leading to inconsistent user experiences.
Innovation Solution
A system is developed to determine a personalization metric by analyzing hash representations of presented content across user profiles, allowing for the adjustment of personalized content based on user feedback and preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If speech processing systems provide personalized content to users, then user engagement and satisfaction improve, but system complexity and computational resources increase
Solution Approach 1:
The patent replaces complex manual analysis and tracking of user interactions with automated machine learning models and natural language processing systems. These computational systems automatically analyze user feedback, determine personalization metrics, and adjust content delivery without requiring complex manual intervention or system reconfiguration.
Solution Approach 2:
The system automatically monitors user interactions, collects feedback, and adjusts personalization levels without requiring explicit user configuration. The speech processing system self-adjusts by analyzing user responses and autonomously determining appropriate personalization metrics, reducing the need for complex external control mechanisms.
2Measurement precision
If the system tracks and analyzes user feedback for personalization, then content relevance improves, but data processing time and computational load increase
Solution Approach 1:
The system pre-processes and stores user feedback data as it is collected, organizing it into structured formats suitable for later analysis. By preparing data in advance and maintaining ready-to-analyze feedback repositories, the system reduces the computational burden and time required when personalization metrics need to be calculated.
Solution Approach 2:
Machine learning algorithms and natural language processing systems automatically analyze user feedback in real-time, replacing manual or batch processing methods. These computational models efficiently extract meaningful patterns from feedback data without requiring extensive human intervention or time-consuming analysis procedures.
3Adaptability or versatility
If the system provides highly personalized content, then user experience quality improves, but consistency across different user profiles becomes difficult to maintain
Solution Approach 1:
The patent implements a universal personalization framework that applies consistent methodology and metrics across all user profiles. The same speech processing algorithms, feedback analysis methods, and personalization calculation approaches are used for every user, ensuring methodological consistency even as personalized content varies by individual preferences and behaviors.
Data Source
AI summary
Techniques for determining a level of personalization for content presented to users are described. A system may determine the level of personalization based on the number of users that receive the same content within a given time period, where if a large number of users receive the same content than the output is not personalized. The personalization level can be used to determine whether more or less personalized content is to be provided to a user, which may be on a domain-basis, user feedback basis, etc. The personalization level can also be used to prompt the user to configure certain settings which can help increase or decrease the amount of personalized content the user receives.