Recommender System User Control via Feedback Segmentation
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Solution Overview
Problem
Implicit recommenders lack user control, making it difficult for users to adjust recommendations to align with their preferences, leading to unwanted program suggestions.
Innovation Solution
A method and system that allows users to generate and store parameter profiles, calculate ratings, and modify them based on user feedback, displaying the number of times user preferences need to be indicated to conform recommendations to their desires, enabling users to select programs and auto-select future preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If an implicit recommender system is used, then the system is easy to use, but the user lacks control over the recommendations
Solution Approach 1:
The system segments the feedback process into discrete, manageable interactions. Instead of requiring continuous manual adjustment of parameters, the system divides user control into individual program rating events, where users provide feedback one program at a time. This segmentation makes the control mechanism simple and easy to use while still providing adaptability.
Solution Approach 2:
The system implements a feedback loop where user ratings of recommended programs are captured and used to automatically adjust the recommender parameters. Users provide feedback by simply rating programs as liked or disliked, and the system uses this feedback to adapt its recommendations, thereby providing user control without requiring complex manual configuration.
2Adaptability or versatility
If an explicit recommender system is used, then the user is in full control, but the system requires complex user input
Solution Approach 1:
The system performs self-service by automatically adjusting its own recommender parameters based on user feedback. Instead of requiring users to manually configure complex preference settings, the system monitors user ratings and autonomously modifies its recommendation algorithm, thereby providing full user control through simple rating actions rather than complex parameter adjustment.
Solution Approach 2:
The system performs preliminary analysis of user feedback patterns to determine when parameter adjustments are needed. By pre-processing user ratings and identifying trends before making adjustments, the system reduces the cognitive load on users and simplifies the interaction process while maintaining adaptability.
3Adaptability or versatility
If the system automatically adjusts parameters based on user feedback, then user control is enhanced, but the number of required user interactions increases
Solution Approach 1:
The system applies partial action by making incremental parameter adjustments based on each user feedback event rather than requiring a fixed number of interactions. The adjustment magnitude is proportional to the feedback strength, allowing the system to adapt with minimal user input when confidence is high, thereby reducing the time loss while maintaining user control.
Solution Approach 2:
The system dynamically adjusts the level of user interaction required based on its confidence in the recommendations. When the system is highly confident in its parameter settings, it requires fewer user interactions to maintain adaptability. When uncertainty increases, it naturally seeks more feedback, creating a dynamic balance between user control and interaction frequency.
Data Source
AI summary
A method and arrangement for modifying settings of a television program recommender displays the rating (recommended or not recommended) for a selected television program, and also displays icons for selection by the user to indicate whether the user agrees or disagrees with the recommendation. The television program recommender then updates its settings based on the user's icon selection. In addition, if the user indicates that he/she disagrees with the recommendation, the television program recommender determines and displays the number of times that the user needs to indicate his/her desires with respect to the selected television program in order for the recommendation to agree with the user's desires.