Question-Answering System Reward Filtering for Recommendation Quality
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Solution Overview
Problem
Existing computer-based question-answering systems face challenges in balancing exploration of new actions with the selection of known actions, leading to non-responsive or less relevant responses due to inadequate handling of user feedback.
Innovation Solution
The system incorporates both noisy and sparse user rewards to filter and update expected rewards and confidence intervals, ensuring that noisy rewards are constrained within defined bounds, thereby improving the quality of recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the QA system uses existing reward handling mechanisms, then the system can process user feedback, but the responses may be non-responsive or less relevant due to inadequate handling of noisy and sparse rewards
Solution Approach 1:
The patent segments user feedback into two distinct types: noisy rewards (frequent but less reliable) and sparse rewards (infrequent but more reliable). By separating these feedback types and processing them through different mechanisms, the system can appropriately weight and integrate each type, improving response relevance while fully utilizing all user feedback.
Solution Approach 2:
The patent introduces an intermediary mechanism (the reward handling module with filtering and integration components) that processes noisy rewards and sparse rewards separately before combining them. This intermediary structure allows the system to filter out noise while preserving valuable information from both feedback types, resolving the contradiction between reliability and information utilization.
2Adaptability or versatility
If the QA system balances exploration and exploitation, then the system can discover new actions, but the complexity of reward handling increases
Solution Approach 1:
The patent implements dynamic adjustment of exploration-exploitation balance by adaptively weighting noisy and sparse rewards based on their respective qualities and availability. The system dynamically modifies its reward handling strategy depending on the current state, feedback types received, and exploration needs, allowing flexibility without requiring a completely complex static structure.
Solution Approach 2:
The patent changes key parameters of the reward handling mechanism, specifically the weighting factors for noisy versus sparse rewards. By adjusting these parameters dynamically based on feedback quality and exploration requirements, the system achieves adaptability while managing complexity through parameter tuning rather than structural overcomplication.
Data Source
AI summary
A computer-based question-answering system is capable of receiving a user input specifying a noisy reward and a sparse reward. The noisy reward and the sparse reward are received responsive to an initial recommendation generated by a computer-based recommendation system. A filtered noisy reward is generated by filtering the noisy reward based on an upper bound for the sparse reward or a lower bound for the sparse reward. A final reward is generated based on the filtered noisy reward and the sparse reward. An expected reward and a confidence interval for each of a plurality of candidate recommendations are updated based on the final reward. A subsequent recommendation generated by the computer-based recommendation system is provided based on the expected reward as updated and the confidence interval as updated for each candidate recommendation of the plurality of candidate recommendations.