Debugging Tool for Recommendation Systems Using Segmented Logging
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Solution Overview
Problem
Machine-learned recommendation systems in job search platforms face challenges in debugging erroneous recommendations due to the time gap between when errors occur and when they are reported, making it difficult to recreate the error conditions as inputs to the model may have changed during this period.
Innovation Solution
A debugging tool that logs information including the model used, user identification, and timestamp for each recommendation, allowing administrators to replay the recommendation process and identify the source of errors without the need to log all features, thus enabling precise error analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all features are logged for every recommendation, then error recreation precision is improved, but system complexity and storage requirements worsen
Solution Approach 1:
The patent segments the logging approach by creating two distinct logging mechanisms: a detailed debug log that captures all features and model states for error analysis, and a separate summary log that records only essential information (user ID, job ID, recommendation score, timestamp). This segmentation allows the system to maintain high error recreation precision when needed while avoiding the complexity of continuously logging all features during normal operation.
Solution Approach 2:
The patent implements preliminary action by pre-computing and storing feature snapshots at the time of each recommendation before the error occurs. When an error is reported, these pre-stored feature snapshots can be immediately retrieved and used for error recreation without needing to log all features continuously, thus reducing system complexity while maintaining error analysis capability.
2Difficulty of detecting and measuring
If detailed feature logging is implemented, then debugging capability is improved, but data processing time and storage costs worsen
Solution Approach 1:
The patent extracts only the essential debugging information needed for error analysis from the complete feature set. Instead of processing and storing all features, the system extracts and stores specifically the feature values, model parameters, and recommendation scores at the time of each recommendation. This extraction approach maintains full debugging capability while significantly reducing data processing time and storage requirements.
Solution Approach 2:
The patent creates lightweight, disposable debug log entries that are generated only when errors occur or at scheduled intervals, rather than maintaining continuous detailed logging. These short-living log entries are processed and analyzed only when needed for debugging, minimizing data processing time while preserving the ability to detect and measure errors when they occur.
3Measurement precision
If the time gap between error occurrence and reporting is reduced, then error recreation accuracy is improved, but system responsiveness requirements worsen
Solution Approach 1:
The patent implements a feedback mechanism where the system automatically detects when a recommendation error occurs and immediately triggers an error log entry. This real-time feedback loop ensures that error information is captured at the exact moment of occurrence, maintaining high error recreation accuracy without requiring the system to respond faster than normal operational speed, as the logging is triggered automatically by the error condition itself.
Data Source
AI summary
In an example embodiment, a debugging tool is provided that includes logging functionality to allow a machine learned model administrator to replay machine learned recommendation model executions in order to identify points of error, without the scaling difficulties that would be involved in logging all features used in every recommendation.