Modeling Tag-Aware Recommendations Based on User Preferences
Why this work is in the frame
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Bibliographic record
Abstract
A recommender system aims at recommending items that users might be interested in. With an increasing popularity of social tagging systems, it becomes urgent to model recommendations on users, items, and tags in a unified way. In this paper, we propose a framework for studying recommender systems by modeling user preferences as a relation on (user, item, tag) triples. We discuss tag-aware recommender systems from two aspects. On the one hand, we compute associations between users and items related to tags by using an adaptive method and recommend tags to users or predict item properties for users. On the other hand, by taking the similarity-based recommendation as a case study, we discuss similarity measures from both qualitative and quantitative perspectives and k-nearest neighbors and reverse k-nearest neighbors for recommendations.
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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 | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 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 it