An Image Classification and Retrieval Algorithm for Product Display in E-Commerce Transactions
Bibliographic record
Abstract
E-commerce has unmatched advantages over conventional ways of consumption, but it also restricts the amount of contact that customers have with the things they are purchasing. The first step in the transaction product information flow is an effective transaction product display image, which is crucial for product sales. The influence of the emotions present in the current transaction product display image on the buyer's purchase intention has not been established, and as the number of commodity classes rises, there is also a lack of scientific guidance regarding the buyer's efficient retrieval of transaction products. Therefore, this work investigates image classification and retrieval methods for product presentation in e-commerce transactions. A robust network architecture was created for the e-commerce transaction product display and image classification. The image polarity emotion feature extraction backbone module, the polarity emotion intensity perception module, and the emotion feature fusion classification module are the three specific components of the model. The network training approach is an innovation to address the issue that the polarity emotion intensity cannot be effectively conveyed in the e-commerce transaction product display image. The research improves the design of the similarity retrieval algorithm for e-commerce transaction product display images, which increased the retrieval effectiveness for buyers. The correctness of the classification model and retrieval approach was confirmed by the experimental results.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".