MPIS: maximal-profit item selection with cross-selling considerations
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
In the literature of data mining, many different algorithms for association rule mining have been proposed. However, there is relatively little study on how association rules can aid in more specific targets. One of the applications for association rules - maximal-profit item selection with cross-selling effect (MPIS) problem - is investigated. The problem is about selecting a subset of items, which can give the maximal profit with the consideration of cross-selling. We prove that a simple version of this problem is NP-hard. We propose a new approach to the problem with the consideration of the loss rule - a kind of association rule to model the cross-selling effect. We show that the problem can be transformed to a quadratic programming problem. In case quadratic programming is not applicable, we also propose a heuristic approach. Experiments are conducted to show that both of the proposed methods are highly effective and efficient.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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