Retail Listing Escalation Detection Using Topic and Keyword Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Online marketplaces face challenges in detecting and addressing retail product listings that may be offensive, non-compliant, or counterfeit, which can negatively impact brand reputation due to publicly shared content on social media or news platforms.
Innovation Solution
A system utilizing machine learning models for topic and escalation classification, combined with an ML explanation engine, to analyze online content, generate keyword lists, and provide real-time alerts for potential escalation events, enabling proactive removal of problematic products.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual monitoring of product listings is used, then detection accuracy can be maintained, but the system cannot scale to handle large volumes of content and responds slowly to escalation events
Solution Approach 1:
The patent introduces ML explanation engines as intermediary components between the content aggregation system and the escalation detection models. These engines provide interpretable explanations for model predictions, enabling human reviewers to efficiently verify and correct automated detections while maintaining high throughput. The intermediary layer bridges the gap between automated speed and human accuracy.
Solution Approach 2:
The detection system is segmented into multiple specialized components: topic classification models for categorizing content, escalation detection models for identifying potential issues, and ML explanation engines for interpretability. Each segment handles specific aspects of the detection task, allowing parallel processing and maintaining accuracy while scaling to large volumes of content.
2Extent of automation
If automated ML models are deployed for escalation detection, then detection speed and scalability improve, but the complexity of the system increases and requires sophisticated infrastructure
Solution Approach 1:
The ML explanation engines operate autonomously, automatically generating interpretable explanations for model predictions without requiring manual configuration or intervention. The system self-manages the complexity of deploying and maintaining multiple ML models, handling model training, deployment, and monitoring automatically, which reduces the operational burden despite high automation levels.
3Reliability
If multiple ML models are used for topic and escalation classification, then detection comprehensiveness improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary topic classification before escalation detection. By first categorizing content into topics using specialized models, the system prepares structured information that accelerates subsequent escalation detection. This preliminary action reduces the computational burden on the final detection stage and enables faster overall processing while maintaining comprehensive detection capabilities.
Data Source
AI summary
The systems and methods provided for automatic retail product listing escalation event detection. The method includes aggregating content items from online resources, determining a topic classification of a content item based on a first machine learning (ML) model, determining an escalation classification of the content item based on a second ML model, generating a keyword list including a plurality of keywords from the content item and scores associated with each of the plurality of keywords based on the topic classification and the escalation classification of the content item, selecting a list of top keywords based on performing a keyword frequency analysis on a plurality of keyword lists associated with the plurality of content items, and providing a detected escalation event.