User Experience Analysis System for Personalized Content Recommendations
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Solution Overview
Problem
Current content recommendation mechanisms fail to provide personalized recommendations as they do not consider the psychological, biological, and emotional aspects of user experiences, leading to irrelevant content suggestions for users.
Innovation Solution
A method and system that analyze qualitative and quantitative user experience data using a combination of qualitative and quantitative analysis algorithms to generate personalized content recommendations by identifying positive appraisal categories and matching them with coded content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If recommendation mechanisms use browsing histories and purchasing histories of existing users, then recommendations can be generated based on aggregate user behavior, but the recommendations fail to be personalized to individual users' emotional and psychological preferences
Solution Approach 1:
The system changes the parameters for measuring user preferences from objective behavioral metrics (browsing history, purchasing history) to subjective experiential metrics (emotional responses, psychological appraisals). This involves collecting and analyzing qualitative data about user emotional states and psychological responses to content, thereby enabling personalized recommendations that reflect individual users' actual preferences rather than aggregate behavior patterns.
2Measurement precision
If the system analyzes qualitative and quantitative user experience data, then personalized recommendations can be generated, but the system complexity increases
Solution Approach 1:
The system segments the analysis process into distinct components: qualitative analysis algorithms that process open-ended user responses and emotional data, quantitative analysis algorithms that process measurable experience data, and a matching module that integrates both to generate recommendations. This segmentation allows each component to specialize in specific data types while maintaining overall system manageability.
Solution Approach 2:
The system introduces an intermediary processing layer that translates diverse user experience data (both qualitative and quantitative) into a unified representation format. This intermediary layer standardizes the data before matching it with content profiles, reducing the complexity of handling multiple data types while preserving the richness needed for accurate personalization.
3Ease of manufacture
If current recommendation systems rely on aggregate user behavior data, then implementation remains relatively simple, but user satisfaction decreases due to irrelevant recommendations
Solution Approach 1:
The system performs preliminary analysis of user experience data to build comprehensive user profiles before generating recommendations. By pre-processing and storing qualitative and quantitative experience data in a structured format, the system prepares the foundation for accurate matching later, making the complex analysis process manageable and scalable while improving recommendation relevance.
Data Source
AI summary
Example embodiments include User Experience Analysis (UEA) system and method. The system and method are employed to qualitatively and quantitatively analyze user experiences, in order to provide electronic content recommendations. The system and method may be employed to provide user specific content recommendations that are based on a determined value-congruence, or relevancy, between electronic content and the recommendation profile generated for the user. The recommendation profile may be determined based on qualitatively and quantitatively analyzing positive appraisal sensations. The positive appraisal sensations may be associated with the user's physiological and/or psychological responses to predetermined content consuming experiences presented to the user, according to the embodiments.