Optimal recommendation sets: covering uncertainty over user preferences
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.045 | 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 it