Content Recommendation Using Explicit Mood Feedback
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current social networks lack user control over the emotions elicited by recommended content, often using engagement tactics that can induce negative feelings, and search engines fail to provide content that aligns with user-defined sentiments.
Innovation Solution
A system and method that allows users to associate reactions (emojis or custom icons) with desired display levels, enabling personalized content selection based on user-defined moods and multi-sentiment consensus categorization, using a multi-sentiment search engine to deliver content that matches desired emotional responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the recommendation engine shows content that engages the user by eliciting negative emotions, then user attention is prolonged and more advertisements may be shown, but the user's well-being is detrimental
Solution Approach 1:
The system dynamically adjusts content recommendations based on real-time user feedback. Users can react to content with various emotions and adjust desired display levels, allowing the recommendation engine to adapt its strategy from engagement-maximization to user-preference alignment, resolving the contradiction between attention duration and well-being
Solution Approach 2:
The patent implements explicit feedback mechanisms where users react to content with emotions and specify desired display levels. This feedback loop enables the recommendation engine to learn user preferences and adjust content selection, allowing users to control the balance between engaging content and emotionally healthy content
2Ease of operation
If the system uses explicit feedback to determine content recommendations, then user control over content is improved, but the system complexity increases
Solution Approach 1:
The system processes and utilizes user feedback automatically through the recommendation engine. Users provide feedback through simple reactions and the system self-adjusts content recommendations without requiring complex user configuration or manual intervention, maintaining ease of operation while implementing sophisticated personalization
Data Source
AI summary
Existing social networks employ implicit and explicit feedback to determine which content to show a user by selecting content designed to maintain the user's attention as long as possible so that more advertisements may be shown between content. To prolong a user's engagement, the content recommendation engine may show content that engages the user through negative emotional states to the detriment of the user's well-being. In an embodiment, a method includes associating (a) a reaction received from a user via a user interface to a unit of content selected by a recommendation engine and presented in the user interface, and (b) a desired display level of the reaction to the user. The reaction is represented by a user-selected icon or a user-selected string. For example, the present system improves social networks by displaying content that elicits the desired emotion of the user, thus empowering the user and enhancing user well-being.