Natural Language Processing-Based Product Category Classification Model for E-Commerce
Bibliographic record
Abstract
In scope of the great dynamism that the world of e-commerce gained with the post-pandemic changes on customer behavior, extending the customer’s time on site has become much more valuable. With regards to creating a better understanding of the online shopper’s intent on site, an effective search engine is the best tool for improving the user experience. A useful search engine needs to produce fast results and do intent analysis of customers correctly to present successful recommendations. In this study, BERT, ELECTRA and RoBERTa which are language models that give successful results in similar problems and pre-training libraries in Turkish languages are used for developing text classification and customer intent analysis models based on e-commerce product categories. The results of these deep learning models, which were optimized using the product description and comment libraries of the selected e-commerce brand, are shared in a comparative way, and the results of our e-commerce end-user intent analysis model, which was tested on user search history, were detailed on the basis of product category. The developed model can be used for e-commerce product prioritization in Turkish language, as well as creating an infrastructure for the development of intent analysis models that are able to predict as far as the main product that the user wants to display in searches containing multiple products.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".