Product Title Inaccuracy Detection via String and ML Algorithms
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Solution Overview
Problem
Inaccurate product titles in online shopping platforms hinder consumer experience by prolonging search times and reducing recommendation quality, as sellers often include irrelevant or misleading information in product titles to manipulate search results.
Innovation Solution
A computer-implemented system that uses a string algorithm and machine learning algorithms to identify and predict product types associated with a product title, detecting inaccuracies by analyzing the title and potentially associated images, and outputs notifications to remote devices for correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If sellers include irrelevant words or inaccurate information in product titles to manipulate search results, then search result manipulation capability is improved, but product title accuracy deteriorates
Solution Approach 1:
The system performs preliminary detection of product title inaccuracies before they affect search results. By using string algorithms to identify product types and machine learning algorithms to predict accurate product types, the system proactively flags inaccurate titles, preventing sellers from manipulating search results with misleading information.
Solution Approach 2:
The system implements a feedback mechanism where detected inaccuracies are communicated back to sellers through notifications. This feedback loop enables sellers to correct inaccurate information in their product titles, improving overall title accuracy while maintaining the ability to detect and prevent future manipulation attempts.
2Measurement precision
If manual monitoring and filtering of product titles is performed, then product title accuracy can be improved, but time consumption and operational complexity increase
Solution Approach 1:
The system enables automated self-service detection of product title inaccuracies without requiring manual intervention. The string algorithm automatically identifies product types mentioned in titles, while the machine learning algorithm predicts the correct product type, allowing the system to autonomously detect and flag inaccuracies for seller review.
Solution Approach 2:
The system replaces manual mechanical review processes with automated computational algorithms. Instead of human reviewers manually examining each product title, the string algorithm and machine learning model automatically analyze titles, identify product types, and detect inaccuracies, dramatically reducing time consumption while maintaining or improving detection accuracy.
3Productivity
If automated detection systems are implemented, then operational efficiency is improved, but system complexity increases
Solution Approach 1:
The detection system is segmented into distinct functional modules: a string algorithm component for identifying product types mentioned in titles, a machine learning component for predicting accurate product types, and a detection component for comparing these results and flagging inaccuracies. This segmentation allows each module to perform its specific function efficiently while reducing overall system complexity through modular design.
Data Source
AI summary
Systems and methods are provided for detecting inaccuracy in a product title, comprising identifying, by running a string algorithm on a title associated with a product, at least one product type associated with the product, predicting, using a machine learning algorithm, at least one product type associated with the product based on the title, detecting an inaccuracy in the title, based on at least one of the identification or the prediction, and outputting, to a remote device, a message indicating that the title comprises the inaccuracy. Running the string algorithm may comprise receiving a set of strings, generating a trie based on the received set of strings, receiving the title, and traversing the generated trie using the title to find a match. Using the machine learning algorithm may comprise identifying words in the title, learning a vector representation for each character n-gram of each word, and summing each character n-gram.