Subject Line Generation System with Sensitivity and Similarity Filtering
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Solution Overview
Problem
Current methods for generating subject lines for electronic messages lack efficiency in filtering out sensitive content, managing character length, and ensuring diversity, which can impact the effectiveness of electronic campaigns.
Innovation Solution
A computer-implemented method and system that preprocesses information for electronic campaigns, uses a generative text engine model to generate subject lines, filters out sensitive content, computes similarity scores to reduce redundancy, and applies a fine-tuned enhancer model to enhance selected subject lines, ensuring they are displayed to merchants in an optimized format.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a generative text engine model generates a large number of subject lines, then the quantity and diversity of subject lines improve, but the complexity of filtering and processing increases
Solution Approach 1:
The filtering system is segmented into multiple independent components: a sensitivity detector that identifies sensitive content, a similarity calculator that compares subject lines against each other and against the description, and a selector that chooses the final M subject lines. This segmentation allows each component to handle a specific aspect of filtering independently, managing the complexity of processing large quantities of generated subject lines.
Solution Approach 2:
The system introduces an intermediary processing layer between the generative model and the final output. This intermediary layer includes the sensitivity detector and similarity calculator that act as mediators to filter and refine the N generated subject lines before selecting M final subject lines, thereby managing the complexity of handling large quantities of generated content.
2Reliability
If subject lines are filtered to eliminate sensitive content and reduce redundancy, then the quality and appropriateness improve, but the processing time increases
Solution Approach 1:
The system performs preliminary filtering actions during the subject line generation process. The sensitivity detector and similarity calculator evaluate subject lines as they are generated or in batches, rather than waiting until all N subject lines are completely generated. This preliminary action reduces the overall processing time by identifying and eliminating unsuitable subject lines earlier in the workflow.
Solution Approach 2:
The system employs automated self-service mechanisms where the sensitivity detector automatically identifies and flags sensitive content without human intervention, and the similarity calculator automatically compares subject lines against each other and against the description to identify redundancies. This automation reduces processing time compared to manual filtering while maintaining high quality standards.
3Adaptability or versatility
If similarity scores are calculated between all subject lines to eliminate redundancy, then the diversity of subject lines improves, but the computational complexity increases
Solution Approach 1:
The system applies similarity calculation selectively rather than uniformly to all subject lines. The similarity calculator focuses on comparing subject lines that are locally relevant or potentially similar based on initial filtering criteria. This local quality approach ensures diversity by concentrating computational resources on the most relevant comparisons rather than calculating all possible pairs, thereby reducing overall computational complexity while maintaining diversity.
4Productivity
If multiple models (generative model, sensitivity detector, enhancer model) are used, then the effectiveness and engagement improve, but the system complexity increases
Solution Approach 1:
The system is segmented into distinct functional models: a generative text engine model that creates subject lines, a sensitivity detector that filters inappropriate content, and an enhancer model that improves selected subject lines. Each model is independently trained and optimized for its specific function, which manages system complexity by dividing the overall task into specialized components rather than using a single monolithic system.
Solution Approach 2:
The system employs multi-functional models that can perform multiple operations. The generative model not only creates subject lines but also provides the basis for subsequent filtering and enhancement. The enhancer model can both improve generated subject lines and learn from feedback to enhance future generations. This universality reduces the need for entirely separate systems for each function, managing complexity while maintaining high effectiveness.
Data Source
AI summary
Apparatuses, methods, and systems for generating subject lines for electronic messages of electronic campaigns. One method includes receiving, by a server, information related to the electronic campaign, preprocessing the received information, receiving a plurality of N subject lines generated based on the preprocess received information from a generative text engine model, reducing the N subject lines down to M subject lines, applying a fine-tuned enhancer model to enhance existing content for a selected one or more of the M subject lines, and displaying M subject lines with enhanced existing content to a merchant user. For an embodiment, reducing the N subject lines down to M subject lines includes filtering the N subject line to eliminate subject lines based on content, and generating similarity scores between each of the N subject lines and eliminating one or more subject lines based on a similarity with one or more other subject lines.