Optimal recommendation sets: covering uncertainty over user preferences
Bibliographic record
Abstract
We propose an approach to recommendation systems that optimizes over possible sets of recommended alternatives in a decision-theoretic manner. Our approach selects the alternative set that maximizes the expected valuation of the user’s choice from the recommended set. The set-based optimization explicitly recognizes the opportunity for passing residual uncertainty about preferences back to the user to resolve. Implicitly, the approach chooses a set with a diversity of alternatives that optimally covers the uncertainty over possible user preferences. The approach can be used with several preference representations, including utility theory, qualitative preferences models, and informal scoring. We develop a specific formulation for multi-attribute utility theory, which we call maximization of expected max (MEM). We go on to show that this optimization is NP-complete (when user preferences are described by discrete distributions) and suggest two efficient methods for approximating it. These approximations have complexity of the same order as the traditional k-max operator and, for both synthetic and realworld data, perform better than the approach of recommending the k-individually best alternatives (which is not a surprise) and very close to the optimum set (which is less expected).
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".