Pre-trained ML Model for Ecommerce Title Compression
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Solution Overview
Problem
Existing ecommerce marketplaces face inefficiencies in conversational transactions due to lengthy product titles, leading to user frustration and increased resource consumption in compressing titles, with existing methods being costly and resource-intensive.
Innovation Solution
A method and apparatus using pre-trained machine learning models with multiple layers, including embedding, encoder, and classification layers, trained on replaced-token detection tasks, to compress product titles efficiently, reducing parameter usage and processing time, allowing for accurate and cost-effective title compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If existing systems use database repositories of product titles with lengthy descriptions, then complete product information is provided to users, but interaction time increases and user satisfaction decreases
Solution Approach 1:
The system pre-generates compressed versions of product titles and stores them alongside the original lengthy titles in the database. This preliminary compression allows the system to quickly retrieve and display shortened product names during user interactions, eliminating the need to process and compress titles in real-time while maintaining information availability.
Solution Approach 2:
The product title information is segmented into two representations: the original complete title for information accuracy and a compressed version for quick display. This segmentation allows the system to use different representations for different purposes - the compressed form for rapid interaction and the original form when detailed information is needed.
2Ease of operation
If existing systems compress product titles using traditional methods, then title length is reduced for better user experience, but computational resources and processing time increase significantly
Solution Approach 1:
Title compression is performed in advance during data processing or offline batches, not during user interactions. The compressed titles are pre-computed and stored, so when users interact with the system, the compression task has already been completed, eliminating real-time computational overhead.
Solution Approach 2:
Instead of performing complex compression algorithms during user interactions, the system creates simplified copies of product titles that are stored in the database. These copied compressed versions are then directly used during interactions, avoiding repeated computational expensive operations.
3Productivity
If existing systems use resource-intensive compression methods, then compressed titles can be generated, but implementation cost and complexity increase
Solution Approach 1:
The system uses simple, lightweight compression rules and heuristics that are computationally inexpensive and easy to implement. Rather than deploying complex machine learning models or sophisticated algorithms, the system uses straightforward text processing rules that are easy to maintain and update, sacrificing some compression optimality for significant gains in simplicity and low cost.
Data Source
AI summary
A system for generating compressed product titles that can be used in conversational transactions includes a computing device configured to obtain product title data characterizing descriptive product titles of products available on an ecommerce marketplace and to determine compressed product titles based on the product title data using a machine learning model that is pre-trained using a replaced-token detection task. The computing device also stores the compressed product titles for use during conversational transactions.