Tracking Request Label Prediction via Machine Learning
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Solution Overview
Problem
Conventional techniques fail to accurately detect errors in tracking requests from client devices, leading to inaccurate data being reported to online systems, which results in incorrect inferences and actions being taken by the online system.
Innovation Solution
An online system uses a machine learning model to predict accurate labels for tracking requests by extracting feature vectors from the requests and generating a score indicating the accuracy of the input label, with a label mapping table used to correct incorrect labels and provide accurate reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional techniques are used to process tracking requests, then the system operation is simple, but the measurement precision of tracking data is poor
Solution Approach 1:
A machine learning model is introduced as an intermediary component between the tracking request receiver and the data processing system. The model receives tracking requests, extracts features, and predicts accurate labels, thereby improving measurement precision without requiring changes to the fundamental tracking infrastructure.
Solution Approach 2:
The tracking request processing is segmented into distinct components: feature extraction module, machine learning model, and label prediction module. This segmentation allows each component to specialize in specific tasks, improving overall accuracy while maintaining modular system architecture.
2Measurement precision
If machine learning model is introduced to improve label accuracy, then the measurement precision is improved, but the device complexity increases
Solution Approach 1:
Feature extraction is performed as a preliminary action before label prediction. The system extracts relevant features from tracking requests in advance, which are then fed to the machine learning model. This preliminary processing simplifies the model's task and improves overall efficiency.
Solution Approach 2:
The machine learning model performs self-training and self-optimization by continuously learning from incoming tracking requests and their predicted labels. The system automatically updates its internal parameters without requiring manual intervention, reducing operational complexity over time.
3Reliability
If tracking requests with incorrect labels are processed, then the processing speed is maintained, but the reliability of analysis results deteriorates
Solution Approach 1:
The manual or rule-based label verification process is replaced with a machine learning-based prediction system. The ML model automatically predicts accurate labels by learning patterns from historical data, eliminating the need for time-consuming manual verification while improving reliability.
Solution Approach 2:
The system changes the parameter of label determination from static (pre-defined rules) to dynamic (ML-based prediction). This allows the label accuracy to adapt based on the specific characteristics of each tracking request, improving reliability without adding significant processing time.
Data Source
AI summary
An online system receives tracking requests from client devices interacting with a website. The online system analyzes user interactions with websites using the tracking requests. The online system predicts an accurate label for the web page that caused the tracking request to be generated. The online system uses the accurate label for generating reports describing user interactions with the website. The online system determines a quality of tracking requests generated by the website based on various factors including a number of web pages of the website that generate tracking requests, the type of information provided by the tracking requests, and so on. The online system generates reports describing the quality of the tracking requests. The online system uses a metric indicating the quality of tracking requests of the website to determine whether to use predicted labels instead of labels provided by tracking requests for generating reports of the website.