Message Classifier Training via User Interaction Feedback
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Solution Overview
Problem
Existing email classification systems face challenges in accurately categorizing messages due to limited information and overlapping features among categories, leading to unreliable categorization.
Innovation Solution
The use of user interaction data as a proxy for ground truth to create a trusted message categorization dataset, where messages from consistent senders with few corrections and positive recipient interactions are upweighted, and those with negative interactions are downweighted or excluded, to improve classifier training and evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional message classification systems are used, then messages can be categorized into predefined categories, but the classification accuracy is low due to limited information and overlapping features among categories
Solution Approach 1:
The system collects user interaction data (opens, reads, deletes, moves) as feedback signals to infer ground truth categories. This feedback mechanism allows the system to continuously improve classification accuracy by learning from actual user behavior patterns, resolving the contradiction between limited message information and accurate categorization.
Solution Approach 2:
User interaction data serves as an intermediary between the message content and the ground truth category. Instead of relying solely on message features which have overlapping characteristics, the system uses user behavior as a mediator to infer the true category, thereby improving classification reliability despite feature overlap.
2Measurement precision
If user interaction data is collected and processed to create training datasets, then classifier accuracy improves, but system complexity and data processing requirements increase
Solution Approach 1:
The system extracts only the necessary interaction signals (opens, reads, deletes, moves) from user behavior data, filtering out unnecessary information. This extraction approach creates focused training datasets that improve classifier accuracy without requiring complex processing of all possible user interactions, thereby managing system complexity.
Solution Approach 2:
The system automatically collects, processes, and generates training datasets from user interaction data without requiring manual annotation or external resources. This self-service approach improves classifier accuracy through continuous learning while avoiding the complexity of manual data preparation pipelines.
3Reliability
If messages from consistent senders with few corrections are upweighted, then training dataset quality improves, but bias toward certain senders may be introduced
Solution Approach 1:
The system applies different weighting strategies to different senders based on their consistency and correction rates. High-quality senders whose messages are consistently categorized correctly receive upweighting, while diverse sender coverage is maintained through minimum representation thresholds. This local quality approach improves training dataset quality without completely sacrificing generalization to new senders.
Data Source
AI summary
Systems and methods for classifying messages are provided. Each message in a plurality of messages is classified, thereby independently identifying a message category in a set of message categories for each respective message in the plurality. The plurality of messages is delivered to a plurality of recipients with a designation of the message category of each respective message in the first plurality of messages. A plurality of recipient initiated message interaction events for messages in the first plurality of messages over a predetermined period of time is collected from the plurality of recipients. A message categorization dataset is then constructed from (i) the first plurality of messages, (ii) the designation of the message category of each respective message in the subset of the first plurality of messages, and (iii) the plurality of recipient initiated message interaction events. This message categorization dataset is used to train or evaluate a message classifier.