Solving the M2M Recommendation Problem via Group Multi-Role Assignment
Bibliographic record
Abstract
Many to many (M2M) recommendation is one of the fundamental and important problems in commerce. With respect to this idea, profits, clients and products are inseparable. The traditional Top-N method cannot process the M2M recommendation problem. Therefore, this paper deals with the M2M recommendation as a many to many assignment problem via the group multi-role assignment (GMRA). Based on the concise formalization of Role-based collaboration (RBC) and its E-CARGO model, a successful approach using an (Extended Integer Linear Programming) x-ILP planning method and an improved greedy Top-N algorithm is proposed. These methods are verified by simulation experiments. Their results indicate the practicability of both solutions. As a comparison, the greedy Top-N method is faster than the x-ILP planning method via the PuLP linear planning package of Python. On the hand, the latter outperforms the former in recommendation accuracy.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".