Product Matching with Two-Branch Neural Network Embedding
Bibliographic record
Abstract
E-commerce platforms play a crucial role in facilitating transactions between buyers and sellers, with technological advancements significantly influencing consumer behaviors.Efficiently managing product catalogs is essential, particularly for identifying and matching products across various channels.This paper explores deep learning techniques for product matching, leveraging both text and image modalities to enhance the accuracy and efficiency of this process.We propose a novel approach using a branch neural network embedding space integrated with K-nearest neighbors (KNN), treating image and text as distinct modalities.For text embedding, we utilize pre-trained BERT and CharacterBERT models, while for image embedding, we employ EfficientNet.Our methodology incorporates the ArcFace loss function to enhance intra-class compactness and inter-class discrepancy, thereby improving classification performance.Our results demonstrate that integrating multimodal embeddings with advanced loss functions like ArcFace significantly enhances the performance of product matching systems.This approach offers valuable insights for developing robust e-commerce platforms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".