Spam Detection Framework for Online Marketplaces
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Solution Overview
Problem
Conventional online marketplaces face challenges in accurately detecting spam listings, which can include incorrect or misleading product information, leading to diminished customer experience and reduced effectiveness in filtering out misattributed products.
Innovation Solution
A spam detection framework that utilizes machine learning and natural language processing techniques to compare image attributes extracted from item listings with inputted product information, calculating a confidence score to determine the accuracy of the listing and classify it as spam or not, thereby modifying search rankings and providing notifications for incorrect listings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional spam detection methods are used, then device complexity is reduced, but measurement precision of spam detection deteriorates
Solution Approach 1:
The spam detection system is divided into multiple specialized modules: image attribute extraction module, text attribute extraction module, comparison module, and confidence score calculation module. Each module handles a specific aspect of the detection process, improving overall accuracy while managing complexity through functional segmentation.
Solution Approach 2:
The patent introduces an intermediary confidence score that mediates between the extracted attributes and the final spam classification. This confidence score serves as a buffer that quantifies the degree of mismatch between image and text attributes, enabling more precise detection without requiring overly complex decision logic.
2Measurement precision
If image attributes and text attributes are compared using machine learning, then spam detection accuracy is improved, but computing resources increase
Solution Approach 1:
The system performs partial comparison by focusing on key attributes that are most indicative of spam listings. Rather than comparing all possible attributes equally, the system identifies and compares the most discriminative features, reducing computational energy while maintaining detection accuracy.
Solution Approach 2:
The patent dynamically adjusts comparison parameters and confidence thresholds based on the specific listing context. By changing parameters adaptively rather than using fixed thresholds, the system optimizes computational efficiency while maintaining high detection accuracy across diverse listing types.
Data Source
AI summary
In various example embodiments, a system and method for determining a spam publication using a spam detection system are presented. The spam detection system receives, from a device, an image of an item and an item attribute for the item. Additionally, the spam detection system extracts an image attribute based on the received image, and compares the item attribute and the image attribute. Moreover, the spam detection system calculates a confidence score based on the comparison. Furthermore, the spam detection system determines that the item attribute is incorrect based on the confidence score transgressing a predetermined threshold. In response to the determination that the item attribute is incorrect, the spam detection system causes presentation, on a display of the device, of a notification.