Recommendation of items with inter-dependencies: a course plan recommender system
Bibliographic record
Abstract
In this thesis we address the problem of recommendation in domains where items have strong dependency constraints. We apply our work on the academic courses domain, where dependencies between items are present as explicit prerequisite constraints, implicit ordering patterns, and general course consumption restrictions. We propose a new recommendation approach that combines both ordering patterns of items learned from data and estimated user interests in providing a personalized sequence of recommendations that take into account item inter-dependencies. Our approach is based on modeling the recommendation problem as a Markov Decision Process (MDP), in which the goal is to find a plan with the maximum total reward. We have developed an implementation of our approach as a web-based course recommender system, which recommends academic course course plans to students for a number of subsequent terms. Experiments on real data collected from students of McGill University demonstrate that our approach has better performance compared to other simpler models which rely exclusively on student interests or on course co-occurrence patterns.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".