Item Listing Categorization via Token Symbolization and N-gram Modeling
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Solution Overview
Problem
Conventional e-commerce websites face challenges in accurately categorizing item listings, leading to incorrect product categories due to seller errors or deceptive intent, which hampers the search experience for buyers.
Innovation Solution
A system that parses item listing titles using token symbolization and n-gram modeling to determine dominant n-gram models for various product categories, allowing for automatic categorization and detection of incorrect categorization, and suggesting the correct category for both sellers and buyers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional e-commerce websites rely on seller-managed category selection, then the system is simple to operate, but categorization accuracy deteriorates due to seller errors or deceptive intent
Solution Approach 1:
The system automatically performs categorization by parsing item listing titles and detecting dominant n-gram models, eliminating the need for sellers to manually select categories. The categorization system serves itself by autonomously analyzing title data and assigning appropriate categories without human intervention, thereby improving accuracy while maintaining operational simplicity for users.
Solution Approach 2:
The patent replaces manual mechanical category selection with an automated computational system that uses natural language processing techniques. The system substitutes human-based categorization with algorithmic analysis of title patterns, leveraging n-gram modeling and machine learning to achieve more accurate and consistent categorization results.
2Measurement precision
If the system automatically categorizes items using n-gram modeling, then categorization accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing by pre-computing and storing n-gram models for different product categories during an offline training phase. When new items need to be categorized, the system leverages these pre-computed models rather than performing exhaustive analysis from scratch, significantly reducing real-time processing time while maintaining high accuracy.
Solution Approach 2:
The system applies partial action by focusing the analysis on the most relevant n-gram patterns and dominant models rather than exhaustively evaluating all possible category combinations. This selective approach processes only the necessary computational steps to achieve accurate categorization, reducing overall processing time while maintaining precision.
3Adaptability or versatility
If the system uses token symbolization and n-gram modeling, then it can handle unknown tokens and maintain accuracy, but the system complexity increases
Solution Approach 1:
The system manages complexity by dynamically adjusting the n-gram order and tokenization parameters based on the input data characteristics. When encountering unknown tokens or ambiguous patterns, the system adapts its analysis parameters to maintain categorization accuracy, allowing flexible handling of diverse and unfamiliar item titles without requiring complex manual configuration.
Data Source
AI summary
Techniques for categorizing item listings based on parsing item listing titles are described. According to various embodiments, listing titles of one or more item listings on a marketplace website are accessed, the item listings being associated with a particular product category in a product category structure of the marketplace website. Words in each of the listing titles may then be converted to semantic tokens in a token symbol space, based on a tokenization process. Thereafter, n-gram modeling may be performed on the tokens corresponding to each of the listing titles of the item listings in the particular product category. One or more dominant n-gram models associated with the listing titles of the item listings in the particular product category may then be identified.