Comparing Traditional and Deep Learning Approaches for Product Matching: Performance on Unseen Entities
Bibliographic record
Abstract
Record linkage systems built with deep learning transformer architectures have shown superior performance in product matching tasks but reportedly struggle with unseen entities. This paper investigates whether this performance drop on unseen entities is inherent to all record linkage approaches or specific to classifiers employing representation learning. We compare traditional machine learning classifiers using hand-engineered feature vectors against state-of-the-art deep learning approaches on a Web Data Commons product matching benchmark. Our results demonstrate that while random forests trained on hand-engineered features generally underperform deep learning methods, they maintain consistent performance across seen and unseen entities. Notably, with limited training data, the hand-engineered features approach achieves competitive results on test sets composed of unseen entities. These findings suggest that the seen/unseen evaluation dimension is particularly crucial for assessing representation learning classifiers, while traditional approaches offer more stable performance across different entity exposure scenarios.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".