Base-Variant Grouping Pipeline for Large Catalog Clustering
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Solution Overview
Problem
Existing systems struggle to efficiently group variants of items, such as different sizes and colors of products, in online catalogs, especially when dealing with large scales and frequent updates, as human review is inadequate for massive item catalogs.
Innovation Solution
A scalable pipeline using machine learning models, including text and image embeddings, k-nearest neighbors, elastic search, logistic regression, and Siamese networks, to automatically suggest and validate base variant groups, reducing the need for manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human review is used to group item variants, then grouping accuracy can be maintained, but the system cannot scale to handle massive item catalogs efficiently
Solution Approach 1:
The system segments the variant grouping task into multiple processing stages: initial automated grouping using machine learning models, validation phase with human reviewers focusing only on uncertain cases, and iterative refinement. This segmentation allows most items to be processed automatically while human reviewers concentrate on edge cases, thereby scaling the system without sacrificing accuracy.
Solution Approach 2:
Machine learning models serve as intermediaries between the raw item data and human reviewers. The models pre-process and group the majority of items, providing suggested groupings that human reviewers can validate or correct. This intermediary layer filters out routine cases, allowing human expertise to be applied more efficiently to complex or ambiguous situations.
2Measurement precision
If more human reviewers are added to validate variant groups, then grouping precision improves, but resource burden and costs increase
Solution Approach 1:
Instead of requiring full human review of all variant groups, the system applies partial action by having human reviewers validate only a subset of groups that fall below a confidence threshold or exhibit ambiguous characteristics. The machine learning models handle the majority of clear-cut cases automatically, reducing the quantity of human resources needed while maintaining adequate precision for the overall system.
3Productivity
If automated machine learning models are used for variant grouping, then processing efficiency increases, but grouping accuracy may deteriorate without human validation
Solution Approach 1:
The system implements feedback loops where human reviewer corrections and validations are fed back into the machine learning models for retraining and refinement. This continuous feedback mechanism allows the automated models to learn from human expertise and improve their accuracy over time, gradually reducing the need for human intervention while maintaining or enhancing grouping precision.
Solution Approach 2:
The machine learning models perform preliminary grouping actions on all item variants before human review. This preliminary action establishes a baseline set of groupings that can be quickly validated or corrected by human reviewers, rather than starting from scratch. The preliminary automated processing handles routine cases efficiently, reserving human resources for cases requiring more careful judgment.
Data Source
AI summary
A system comprises one or more processors and non-transitory computer-readable media storing computing instructions that, when executed, perform operations comprising: generating an adjacency list for candidate items using a distance threshold with the maximum allowable neighbor distance equal to a Siamese model cut-off. The operations can also comprise loading data for the candidate items in the adjacency list and generating graphs of the candidate items in the adjacency list. The operations further can comprise determining, using breakdown logic, first graphs of the graphs that exceed a predetermined size, and building hierarchy dendrograms of nested subclusters of the first graphs. The operations additional can comprise determining cut-off values based on p-th percentiles of density for the first graphs, and identifying recommended variant groups of the candidate item in the nested subclusters of the hierarchy dendrograms below the cut-off values. Other embodiments are disclosed.