Recommendation of items with inter-dependencies: a course plan recommender system
Bibliographic record
Abstract
DEDICATIONTo friends and all people of Syria who are struggling for their basic rights of dignity and freedom, my parents, and my dear wife.ABR ÉG É Dans cette thèse, nous abordons le problème de recommandation dans les domaines où les éléments ont des fortes contraintes de dépendance.Nous appliquons notre travail sur le domaine des cours académiques où les dépendances entre les éléments sont présentes comme contraintes de condition préalable explicites, pattern de l'arrangement implicite, et restrictions générale de la consomption de cours.Nous proposons une nouvelle approche au problème de recommandation qui combine l'arrangement des articles appris á partir des données et les intérêts de l'utilisateur, estimés pour fournir une séquence personnalisée des recommandations, qui prend en compte les interdépendances des éléments.Notre approche est base sur la modélisation du problème de recommandation comme processus de décision Markovien (MDP) dont le but est de trouver un plan á profit totale maximale.Nous avons implémenté notre approche sur le Web en tant qu'un système qui propose des plans de cours aux étudiants pour un certain nombre de périodes subséquentes.Nos expériences sur des données réelles recueillies auprès des élèves de l'Université McGill démontrent que notre approche est plus performant comparativement aux modles qui ne considèrent que les intérêts d'étudiant ou les patterns de co-occurrence de cours.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".