Personalized Content Recommendation via Emotional Trait Analysis
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Solution Overview
Problem
Current recommendation mechanisms in content provider systems, such as Netflix and Amazon, fail to provide personalized content recommendations as they rely on browsing histories of other users, which may not align with individual user preferences, and do not consider psychological, biological, and emotional markers associated with consumer experiences.
Innovation Solution
A system and method that utilizes a User Experience Analysis (UEA) system to collect and analyze qualitative and quantitative data on user experiences, including physiological and psychological reactions, to generate personalized content recommendations based on value-congruence between user reactions and categorized content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If recommendation mechanisms rely on browsing histories of other users, then recommendations can be generated based on existing data, but the recommendations may not align with individual user preferences and emotional traits
Solution Approach 1:
The patent replaces traditional mechanical data-processing recommendation systems with a system that integrates psychological, biological, and emotional analysis. Instead of merely processing browsing histories, the system analyzes user emotional states, physiological responses, and psychological profiles to generate personalized recommendations that align with individual user preferences and emotional traits.
Solution Approach 2:
The patent changes the parameters used for recommendation generation from basic browsing behavior metrics to include emotional intensity, psychological characteristics, and biological responses. This transformation of parameters enables the system to capture deeper aspects of user preferences and provide more accurate personalized recommendations.
2Adaptability or versatility
If large amounts of multimedia content are made accessible to users, then content variety and user choice increase, but searching through and organizing content becomes cumbersome and time-consuming
Solution Approach 1:
The patent applies preliminary action by analyzing user emotional states and psychological profiles before content consumption occurs. The system pre-processes and organizes content based on predicted user preferences and emotional needs, so that when users search for content, the results are already tailored to their individual characteristics, significantly reducing search time and effort.
Solution Approach 2:
The system incorporates feedback loops that continuously monitor user responses, emotional reactions, and consumption patterns. This feedback is used to refine and update user profiles and content recommendations in real-time, creating a dynamic system that adapts to changing user preferences and minimizes search time through increasingly accurate predictions.
3Productivity
If recommendation systems use collaborative filtering based on user behaviors, then recommendations can be generated efficiently, but they fail to consider psychological, biological, and emotional markers associated with consumer experiences
Solution Approach 1:
The patent merges multiple analysis approaches including collaborative filtering, psychological profiling, biological response analysis, and emotional state monitoring into a unified recommendation system. This integration allows the system to maintain the efficiency of data-driven algorithms while adding the precision of multi-dimensional human behavior analysis, achieving both productivity and measurement precision.
Solution Approach 2:
The patent creates a composite recommendation system that combines different analytical materials and methods. Just as composite materials combine different properties to achieve superior performance, the system combines computational efficiency from algorithmic filtering with the precision of psychological and biological analysis to produce recommendations that are both efficient to generate and accurate in aligning with user emotional traits.
Data Source
AI summary
Example embodiments include systems and methods for transmitting, by one or more processors coupled to memory, a request associated with a first application programming interface endpoint to an application programming interface server. The systems and methods may include retrieving, by the application programming interface server, data from one or more databases responsive to the request. The systems and methods may include transmitting, by the application programming interface server, a response to the one or more processors, the response including the data associated with at least one of independent recommendations and rankings.