Variant Item Detection Using Text Alignment and Graph Clustering
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Solution Overview
Problem
Existing e-commerce platforms face challenges in efficiently identifying and grouping variant products from unstructured item data, such as product titles and descriptions, which hinders effective search results and product detail page management.
Innovation Solution
A variant detection component that aligns item pairs using text alignment algorithms, assigns similarity scores, and performs graph clustering to determine sets of variant items, allowing for the consolidation of search results and simplification of product detail pages by identifying items that differ by attributes like color and size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If text alignment algorithms are applied to unstructured item data, then similarity scoring accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary text alignment and similarity scoring for item pairs before final variant determination. By pre-processing the text alignment step, the system prepares similarity scores in advance that can be used for efficient variant set identification without requiring full re-processing during query operations.
Solution Approach 2:
The patent replaces manual or rule-based text comparison mechanisms with automated text alignment algorithms that use computational methods to score similarity. This substitution enables more accurate and consistent similarity measurement while managing processing complexity through algorithmic optimization.
2Measurement precision
If graph clustering is performed to identify variant sets, then product grouping accuracy is improved, but computational complexity increases
Solution Approach 1:
The system segments the product catalog into manageable groups by first identifying individual variant items through text alignment, then clustering these segmented items into variant sets using graph clustering algorithms. This segmentation approach reduces the complexity of working with the entire catalog at once by processing items in smaller, more manageable units.
Solution Approach 2:
The patent introduces an intermediate similarity score calculation step that serves as a mediator between raw item data and final variant set identification. By computing similarity scores as an intermediate representation, the system simplifies the complex task of identifying variants into a more manageable two-step process: scoring followed by clustering.
3Measurement precision
If all item data is processed to identify variants, then search result accuracy is improved, but data processing volume increases
Solution Approach 1:
The system applies partial action by processing only the necessary portions of item data - specifically comparing items against each other in pairs rather than processing every attribute of every item independently. This partial processing approach maintains search accuracy while reducing overall data processing volume through targeted comparisons.
Solution Approach 2:
The patent merges multiple data processing operations into a unified variant identification process. By combining text alignment, similarity scoring, and graph clustering into a single integrated workflow, the system reduces redundant processing steps and minimizes total data processing volume while maintaining comprehensive search accuracy.
Data Source
AI summary
Various embodiments of a method and system for determining sets of variant items are described. Various embodiments may include a system configured to generate multiple item pairs each corresponding to a particular item and another item determined to be similar to the particular item. For the particular item and the other item, each item pair may include a respective sequence of text strings (e.g., a title). For each item pair, the system may perform a corresponding text alignment and determine one or more misalignments of the item pair. The system may also assign a similarity score to each item pair; the similarity score may be dependent on the misalignment(s) determined for the particular item pair. Based on each aligned item pair and the similarity score assigned to that aligned item pair, the system may generate an indication specifying that each of a set of items are variants of each other.