Effective sampling and learning for mallows models with pairwise-preference data
Bibliographic record
Abstract
Learning preference distributions is a critical problem in many areas (e.g., recommender systems, IR, social choice). However, many existing learning and inference methods im-pose restrictive assumptions on the form of user preferences that can be admitted as ev-idence. We relax these restrictions by considering as data arbitrary pairwise comparisons of alternatives, which represent the fundamental building blocks of ordinal rankings. We develop the first algorithms for learning Mallows models (and mixtures thereof) from pair-wise comparison data. At the heart of our technique is a new algorithm, the generalized repeated insertion model (GRIM), which allows sampling from arbitrary ranking distribu-tions, and conditional Mallows models in particular. While we show that sampling from a Mallows model with pairwise evidence is computationally difficult in general, we develop approximate samplers that are exact for many important special cases—and have provable bounds with pairwise evidence—and derive algorithms for evaluating log-likelihood, learn-ing Mallows mixtures, and non-parametric estimation. Experiments on real-world data sets demonstrate the effectiveness of our approach.1
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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 | 0.025 | 0.099 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.012 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".