Learning community-based preferences via dirichlet process mixtures of Gaussian processes
Bibliographic record
Abstract
Bayesian approaches to preference learning using Gaussian Processes (GPs) are attractive due to their ability to explicitly model uncertainty in users ’ latent utility functions; unfortunately existing techniques have cubic time complexity in the number of users, which renders this approach intractable for collaborative preference learning over a large user base. Exploiting the observation that user populations often decompose into communities of shared preferences, we model user preferences as an infinite Dirichlet Process (DP) mixture of communities and learn (a) the expected number of preference communities represented in the data, (b) a GPbased preference model over items tailored to each community, and (c) the mixture weights representing each user’s fraction of community membership. This results in a learning and inference process that scales linearly in the number of users rather than cubicly and additionally provides the ability to analyze individual community preferences and their associated members. We evaluate our approach on a variety of preference data sources including Amazon Mechanical Turk showing that our method is more scalable and as accurate as previous GP-based preference learning work. 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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".