Automatic classification of categories for Great Hobbies Inc. based on Vendor Information
Bibliographic record
Abstract
This paper focuses on developing a product code classification system for Great Hobbies, a retail company specializing in the sale of hobby products. The system was built to provide a quick and efficient way to categorize the large number of products that the company offers. The pairing of products with the category is done by humans taking into account structured and unstructured data. Examples of unstructured data are name and product descriptions; structured data consist of the vendor’s product code. There are over 200 different combinations of department and product codes in Great Hobbies, which makes this pairing a time-consuming process. A machine learning-based approach was used to build the classification models. Multiple models were developed and evaluated based on their accuracy and time complexity. The best model was selected and deployed as a component of a decision support software system that runs on the Shopware platform. The system was designed with three main components: a front-end application, a web API, and backend workers. Results from the implementation show that the models have achieved a high degree of accuracy in classifying product codes into their respective categories.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".