Mood-Score Playlist Adjustment for Stable Content Streaming
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Solution Overview
Problem
Existing content consumption systems lack mechanisms to dynamically adjust media playlists based on user mood, leading to potential negative impacts on user mood and the spread of offensive or negatively rated content, especially on social networking platforms.
Innovation Solution
A system that tracks cumulative mood scores using machine learning models to curate and adjust media playlists, ensuring they maintain a desired mood threshold by incorporating mood analysis of content segments and user feedback, and applies filters to manage content distribution based on user profiles and group dynamics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If content items are randomly selected and streamed continuously on social networking platforms, then content variety and consumption efficiency are improved, but user mood stability and content coherence deteriorate
Solution Approach 1:
The system dynamically adjusts the playlist by continuously monitoring cumulative mood scores and replacing content items that would cause the mood to drop below a threshold. This dynamic adjustment mechanism allows the system to adapt to changing user moods while maintaining overall mood stability, resolving the contradiction between content variety and mood consistency.
Solution Approach 2:
The system uses mood scores as feedback to control content selection. By analyzing mood scores associated with consumed content and using this feedback to determine future content recommendations, the system ensures that content variety does not compromise user mood stability. The feedback loop enables intelligent filtering of content that would negatively impact mood.
2Stability of the object's composition
If mood analysis and dynamic playlist adjustment mechanisms are implemented, then user mood stability and content coherence are improved, but system complexity and computational requirements increase
Solution Approach 1:
The system performs preliminary analysis by pre-calculating and storing mood scores for content items before they are recommended to users. This preliminary action allows the system to have mood evaluation data ready in advance, reducing real-time computational complexity while maintaining the ability to dynamically adjust playlists based on cumulative mood scores.
Solution Approach 2:
The system automatically monitors cumulative mood scores and performs playlist adjustments without requiring manual user input or complex real-time processing. The self-service mechanism uses pre-computed mood scores and simple comparison logic to maintain mood stability, reducing system complexity while achieving the desired outcome.
3Object-affected harmful factors
If content filtering based on mood scores is applied, then negative content spread is limited and user experience is improved, but content diversity and information flow are reduced
Solution Approach 1:
The system changes the parameter of content selection from random or popularity-based to mood-score-based, allowing it to filter out negative content while maintaining overall content diversity. By adjusting the mood threshold parameter dynamically, the system can control the level of filtering applied, balancing negative content reduction with content diversity preservation.
Data Source
AI summary
Systems and methods are described herein for recommending content based on a mood score associated with a user profile. The system accesses the viewing history of the user profile to determine media assets consumed and the mood score associated with each of the consumed media assets of the plurality of media assets. A cumulative mood score is calculated based at least in part to determine if the total score is below a mood threshold. Based on the cumulative mood score being lower than the mood threshold, the system generates for presentation, on the consumer device, one or more media assets with a positive mood score.