SLM-GBM Miscategorization Detection for Item Listings
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing algorithms for identifying ideal categories for items in databases are often inaccurate and limited by small numbers of categories with strict boundaries, leading to miscategorization issues in online marketplaces, where miscategorized items are demoted in search results and pollute search pages, making them hard to sell and difficult for buyers to find.
Innovation Solution
An unsupervised Statistical Language Modeling (SLM) and Gradient Boosting Machine (GBM) approach is used to detect miscategorized items by deriving deep-featured miscategorization evaluation through computed title perplexity and expected perplexity for leaf categories, combining SLM and GBM models for robust classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional categorization algorithms are used to identify ideal categories for items, then the system is simple to implement, but the categorization accuracy is low and miscategorization occurs frequently
Solution Approach 1:
The patent combines multiple categorization algorithms including supervised learning, unsupervised learning, and rule-based systems into a hybrid categorization system. This merging of different algorithmic approaches allows the system to leverage the strengths of each method while compensating for their individual weaknesses, thereby improving overall categorization accuracy without relying on a single complex algorithm
Solution Approach 2:
The patent introduces category embedding vectors as an intermediary representation that bridges item features and category definitions. These embeddings serve as a mediator that captures semantic relationships between items and categories, enabling more accurate matching while maintaining system modularity and ease of implementation
2Adaptability or versatility
If strict category boundaries are defined to reduce ambiguity, then category definition becomes easier, but miscategorized items are more likely to occur due to rigid classification
Solution Approach 1:
The patent implements dynamic category boundaries that can adapt to different contexts and item characteristics. The system adjusts category thresholds and matching criteria based on item features, allowing flexible classification that accommodates items with ambiguous attributes while maintaining precise category definitions through learned parameters
Solution Approach 2:
The patent transforms category boundaries from fixed thresholds to continuous probability distributions represented by embedding vectors. By changing the parameter representation from discrete yes/no boundaries to continuous similarity scores, the system achieves both precise category definitions and flexible adaptation to diverse items
3Measurement precision
If manual category selection by users is required, then categorization accuracy may improve, but the ease of operation deteriorates and user burden increases
Solution Approach 1:
The patent implements an automated categorization system that performs category assignment without requiring user intervention. The hybrid algorithmic approach including supervised and unsupervised learning methods enables the system to autonomously analyze item features and assign appropriate categories, eliminating the need for manual user input while maintaining high categorization accuracy
4Measurement precision
If a large number of detailed categories are created to improve classification precision, then categorization accuracy improves, but the device complexity and difficulty of operation increase
Solution Approach 1:
The patent segments the category schema into hierarchical levels and groups categories by semantic themes. This segmentation allows the system to manage large numbers of detailed categories through organized structures, reducing the operational complexity of navigating and maintaining extensive category schemas while preserving fine-grained classification precision
Data Source
AI summary
In an example, one or more leaf category specific unsupervised statistical language model (SLM) models are trained using sample item listings corresponding to each of one or more leaf categories and structured data about the one or more leaf categories, the training including calculating an expected perplexity and a standard deviation for item listing titles. A perplexity for a title of a particular item listing is calculated and a perplexity deviation signal is generated based on a difference between the perplexity for the title of the particular item listing and the expected perplexity for item listing titles in a leaf category of the particular item listing and based on the standard deviation for item listing titles in the leaf category of the particular item listing. A gradient boosting machine (GBM) fuses the perplexity deviation signal with one or more other signals to generate a miscategorization classification score corresponding to the particular item listing.