Dynamic User Preference Interface for Sequence Recommendations
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Solution Overview
Problem
Conventional digital recommendation systems are inaccurate, inflexible, and inefficient due to their reliance on a 'black-box' approach, failing to consider dynamic user preferences and requiring significant time and resources for training and adjustments.
Innovation Solution
A dynamic user preference interface that modifies a reward function in a recommendation model based on user input, providing personalized and interactive sequence recommendations with visual justifications, allowing for real-time adjustments without retraining the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a black-box approach is used to generate digital recommendations, then the system operation is simplified, but the accuracy of recommendations deteriorates
Solution Approach 1:
The system incorporates user feedback mechanisms where users can indicate preferences and provide feedback on recommendations. This feedback loop allows the system to iteratively improve recommendation accuracy by learning from user responses and adjusting the recommendation generation process accordingly, resolving the contradiction between simplified operation and accurate recommendations.
Solution Approach 2:
The recommendation system is segmented into multiple components including a recommendation generator, a visualizer, and a user preference interface. This segmentation allows each component to be optimized independently, with the recommendation generator focusing on accuracy while the visualizer and interface handle presentation and user interaction, thus maintaining operational simplicity while improving recommendation accuracy.
2Device complexity
If a black-box approach is used to generate digital recommendations, then the system structure is simplified, but the flexibility to adapt to user preferences deteriorates
Solution Approach 1:
The system introduces dynamic elements that allow it to adapt to changing user preferences in real-time. The recommendation generator can modify recommendations based on user feedback, and the visualizer can dynamically adjust visual representations based on user interactions. This dynamic capability provides flexibility while maintaining a relatively simple overall system structure.
Solution Approach 2:
The system introduces an intermediary layer between the recommendation generator and the user interface, which acts as a mediator to translate user preferences into actionable feedback. This intermediary component enables the system to adapt to user preferences without requiring fundamental changes to the core recommendation structure, thus maintaining simplicity while improving flexibility.
3Productivity
If conventional models are used to generate recommendations, then the basic recommendation function is achieved, but the computational resources and time required for training and adjustments deteriorate
Solution Approach 1:
The system extracts the essential recommendation generation function from complex training processes. By using a recommendation generator that can produce recommendations without requiring extensive retraining, the system maintains basic recommendation functionality while significantly reducing the time and computational resources needed for adjustments and updates.
Solution Approach 2:
The system utilizes parameter changes to adapt recommendations without retraining the underlying model. By modifying parameters such as user preference weights and recommendation criteria rather than retraining the entire model, the system achieves flexible adjustments with minimal computational resources and time investment.
Data Source
AI summary
The present disclosure relates to generating and modifying recommended event sequences utilizing a dynamic user preference interface. For example, in one or more embodiments, the system generates a recommended event sequence using a recommendation model trained based on a plurality of historical event sequences. The system then provides, for display via a client device, the recommendation, a plurality of interactive elements for entry of user preferences, and a visual representation of historical event sequences. Upon detecting input of user preferences, the system can modify a reward function of the recommendation model and provide a modified recommended event sequence together with the plurality of interactive elements. In one or more embodiments, as a user enters user preferences, the system additionally modifies the visual representation to display subsets of the plurality of historical event sequences corresponding to the preferences.