Recommender Control System for Data Optimization
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Solution Overview
Problem
Recommender systems face challenges in efficiently managing user interaction data to provide accurate and resource-effective recommendations, particularly in dynamic environments with varying user preferences and high computational costs, and lack external control mechanisms to optimize performance.
Innovation Solution
A recommender control system that ranks user interaction data based on its likelihood of indicating user preferences, allowing for the selection of an optimum amount of data to be sent to the recommender system, thereby optimizing resource usage and recommendation quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all user interaction data is collected and processed for retraining the recommender system, then the accuracy and up-to-date nature of preference predictions is improved, but the system resource consumption (bandwidth and computational cost) increases
Solution Approach 1:
The patent extracts only the most relevant and informative user interaction data for retraining the recommender system, rather than processing all available data. This selective extraction reduces bandwidth and computational resources while maintaining prediction accuracy by focusing on data that provides the most value for updating user preference models.
Solution Approach 2:
The patent applies different processing quality levels to different portions of user interaction data based on their relevance and informativeness. High-value data that significantly impacts preference predictions receives full processing attention, while less critical data is either summarized or excluded, optimizing the balance between accuracy and resource consumption.
2Adaptability or versatility
If the recommender system is retrained frequently with fresh training data, then the adaptability to changing user preferences is improved, but the computational cost and processing time increase
Solution Approach 1:
The patent performs preliminary filtering and selection of training data before the actual retraining process. By pre-identifying and preparing the most relevant user interaction data in advance, the system can execute retraining more efficiently with reduced computational overhead and faster processing times while maintaining adaptability to preference changes.
Solution Approach 2:
The patent applies partial retraining using only the most critical subsets of user interaction data rather than complete retraining on all available data. This partial action approach maintains system adaptability to changing preferences while significantly reducing the time and computational resources required for each retraining cycle.
3Measurement precision
If a large amount of user behaviour data is transferred from client devices to the recommender system, then the quality of recommendations is improved, but the network bandwidth consumption increases
Solution Approach 1:
The patent extracts and transfers only the most informative user behaviour data from client devices to the recommender system. By identifying and selecting only the data that significantly contributes to recommendation quality, the system reduces network bandwidth consumption while maintaining high recommendation accuracy through selective data transmission.
Solution Approach 2:
The patent performs preliminary processing and filtering of user behaviour data at the client device or edge server before transmission. This advance preparation reduces the volume of data that needs to be transferred over the network while preserving the essential information needed for high-quality recommendations, thereby optimizing bandwidth utilization.
Data Source
AI summary
User interaction data is heuristically processed to determine its usefulness to a remote recommender system. The data is ranked according it its likely ability to indicate a user preference towards a particular content item. The amount of data sent to the recommender system can then be limited to data records falling in a predetermined range of usefulness. A controller may be provided to determine what range is necessary so that the minimum amount of training data is provided to the recommender system to drive the recommender system's performance towards a set level.