Automatic Item Categorization via Relevance Clustering
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Solution Overview
Problem
Network-based retailers face difficulties in accurately categorizing items for sale due to numerous possible categories, making it challenging to identify and select the most appropriate categorization.
Innovation Solution
An item categorization service that automatically assigns categories to items based on associated item information using relevance assessments and hierarchical relationships, employing search indices and rules to refine categorizations and determine confidence levels through relevance clustering and category distances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual categorization methods are used, then categorization accuracy can be maintained, but the complexity and time required for categorization increases significantly
Solution Approach 1:
The system enables automatic self-categorization of items by computing devices without requiring manual intervention. The categorization service receives item information, performs automated relevance assessments against search indices, and assigns categories based on hierarchical relationships and confidence level calculations, allowing the system to serve itself rather than relying on human operators.
Solution Approach 2:
The patent replaces manual mechanical categorization processes with automated computational systems. Instead of human reviewers manually analyzing and categorizing items, the system uses automated relevance assessments, search index comparisons, and algorithmic confidence level calculations to perform categorization, substituting mechanical human labor with computational automation.
2Productivity
If automated categorization is implemented, then productivity increases, but measurement precision of categorization may deteriorate
Solution Approach 1:
The system implements feedback mechanisms through confidence level calculations and relevance assessments. The categorization service evaluates the confidence level for each automated categorization decision by comparing item information against search indices and hierarchical relationships. This feedback loop allows the system to assess its own categorization quality and make adjustments, ensuring accuracy while maintaining high productivity through automation.
3Adaptability or versatility
If multiple category options are provided for items, then adaptability of categorization increases, but the difficulty of selecting the most appropriate category increases
Solution Approach 1:
The system performs preliminary actions by pre-establishing search indices containing category information and hierarchical relationships before categorization is needed. The categorization service uses these pre-prepared indices to efficiently evaluate multiple category options and automatically select the most appropriate category based on relevance assessments and confidence level calculations, eliminating the need for manual selection while maintaining adaptability.
Data Source
AI summary
An item categorization service is described that automatically categorizes items of interest to a user. The user may possess an item that they wish to offer for sale using a network-based service. The user may submit item information to the item categorization service to categorize the item of interest. Upon receipt, the categorization service may assess the relevance of the item information to hierarchically organized categories maintained by the network-based service. Categories having the highest relevance may be identified as first category candidates. The deepest common ancestor of the first category candidates may be identified the first category. One or more categories, representing sub-categories of the first category, may be identified as and subjected to relevance assessment. Those sub-categories having the highest relevance may be identified as second category candidates. The deepest common ancestor of the second category candidates may be identified as a second category for the item of interest.