Recommender Control System Data Filtering for Bandwidth Optimization
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Solution Overview
Problem
Recommender systems face challenges in dynamically adapting to changing user preferences due to continuous influx of new data, varying user behavior, and resource constraints, with existing methods lacking efficient control over training data usage and performance optimization.
Innovation Solution
A recommender control system that ranks user interaction data based on its likelihood of indicating user preferences, using heuristics to prioritize data records and limit the amount of data sent to the recommender system, ensuring optimal performance while minimizing resource usage by controlling the amount of training data provided.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all user interaction data is collected and sent to the recommender system for training, then the recommendation accuracy improves, but the network bandwidth consumption and system resource usage increase significantly
Solution Approach 1:
The patent extracts only the most relevant user interaction data records that are most likely to indicate user preferences, rather than transmitting all collected data. This selective extraction reduces network bandwidth consumption while maintaining recommendation accuracy by focusing on high-value data points.
Solution Approach 2:
The patent applies different quality standards to different data records by ranking them based on their likelihood of indicating user preferences. High-ranking records that are more likely to reflect true user preferences are prioritized for transmission, while lower-ranking records are filtered out, creating a quality-based data selection strategy.
2Measurement precision
If user interaction data is collected from a large number of users to improve recommendation quality, then the recommendation accuracy improves, but the computational cost for training increases
Solution Approach 1:
The system extracts and transmits only a limited subset of the most relevant training data records from the large pool of user interactions. This reduces the computational burden on the recommender system during training while preserving the accuracy benefits by focusing on the most informative data points.
Solution Approach 2:
Instead of using all available user interaction data, the system applies partial action by selecting only the necessary portion of data that provides sufficient training signal. This avoids the excessive computational cost of processing complete datasets while maintaining adequate recommendation accuracy.
3Adaptability or versatility
If the recommender system is retrained frequently to adapt to changing user preferences, then the adaptability improves, but the system resource consumption increases
Solution Approach 1:
The system implements periodic retraining at recommended intervals rather than continuous training. Between retraining cycles, the recommender system operates with its current model, reducing computational resource consumption while still adapting to changing user preferences through periodic updates with fresh training data.
Solution Approach 2:
During each retraining cycle, the system extracts and transmits only the most relevant new user interaction data that occurred since the last training. This selective data transmission reduces the resource consumption associated with each retraining operation while maintaining the system's ability to adapt to changing preferences.
4Measurement precision
If comprehensive user preference data is collected to improve recommendation quality, then the recommendation accuracy improves, but the data transmission volume increases
Solution Approach 1:
The system extracts and transmits only a limited number of the most relevant user interaction data records that are most likely to indicate user preferences. This selective extraction significantly reduces data transmission volume while maintaining preference prediction accuracy by focusing on high-value records.
Solution Approach 2:
The patent implements quality-based filtering where data records are ranked according to their likelihood of indicating user preferences. Only high-quality records above certain thresholds are transmitted, creating a quality-over-quantity approach that reduces transmission volume while preserving prediction accuracy.
Data Source
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AI summary
A recommender system (18) is described which is capable of being controlled using training data provided by an external controller (16). The controller (16) is arranged to control the input of training data from client devices (22) to a recommender system (18) to drive the recommender system's performance towards a set level.