Anchor-Guided Contrastive Learning for User Identification Based On Video Preferences
Bibliographic record
Abstract
User identification through aesthetic preferences has gained attention as a promising direction in social behavioral biometrics, offering a non-invasive and privacy-conscious alternative to traditional physiological identifiers. Unlike still images or single-modal inputs, video-based aesthetic preferences provide richer, temporally-aware insights into user behavior, enabling a deeper understanding of personal taste and style. Despite this potential, existing approaches often treat preference items independently and fail to capture the interrelationships within a user’s preference set. To address these limitations, this paper introduces Preference-Aware Set Encoding with Contrastive Personalization (PASE), a novel deep learning framework designed to model user identity based on structured preferred video sets. The proposed method integrates a Cross-Video Attention Encoder to learn co-preference patterns across videos, a User-Anchor Contrastive Loss to align personalized embeddings, and a Cross-Set Mixup Regularization technique to improve generalization by simulating diverse preference scenarios. Evaluation on a curated aesthetic dataset demonstrates that PASE achieves 98.38% identification accuracy, outperforming unimodal and multi-modal baselines. These findings highlight the unique advantages of leveraging video-based aesthetic information for biometric identification, particularly in applications demanding both accuracy and user-friendly privacy safeguards.
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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 | 0.001 |
| 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) | 0.005 | 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 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".