DimReg: Embedding Dimension Search via Regularization for Recommender Systems
Bibliographic record
Abstract
Modern recommender systems aim to identify items that are most pertinent to a particular user and are particularly useful when an overwhelming number of items are present. Feature embedding is essential to deep recommender systems, which constructs memory-efficient and semantically meaningful representations by mapping high-dimensional sparse feature vectors into low-dimensional dense vectors. Most existing systems assign a unified dimension to all feature fields, regardless of the diverse importance of different features, which usually results in sub-optimal performance and high memory usage. In this paper, we propose a low-cost embedding dimension search approach named DimReg for recommender systems, by assessing information overlapping between the dimensions within each feature field and pruning unimportant and redundant dimensions progressively during model training via a two-level polarization regularizer, while introducing minimum overhead. Moreover, our method does not require retraining after embedding dimension search, which significantly reduces the computational cost and is more friendly to deployment in real-world recommender systems. Extensive experiments conducted on multiple CTR (Click Through Rate) prediction tasks demonstrate that our method can efficiently reduce the model parameters up to 98.6%, and achieve strong recommendation performance outperforming existing automated embedding dimension search methods.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".