Hybrid Recommender System for Personalized Pedagogical Resource Recommendations in E-Learning Platforms
Bibliographic record
Abstract
Recommender systems are generally used in several domains, like e-commerce sites and social networks.E-learning systems use recommendation techniques to facilitate and improve online learning.Educational platforms offer users the necessary pedagogical tools to create an enriched learning environment, fostering collaboration and resource sharing.Recommender System faces many challenges.Among issues: (1) cold-start in which new users and/or items having not prior information available in the system; (2) data sparsity where rated items number is very small contrary to unrated items; and (3) scalability where more training data is required.This study presents a recommender system that uses learner criteria, such as learner's past behavior, demographics information, performance data, collaborative filtering, and ratings to suggest pedagogical resources.The proposed system adopts a hybrid approach, combining two primary methods: popularity-based and collaborative filtering-based.This hybrid approach enhances a collaborative filtering approach with popularity to provide a starting point for new users.The popularity-based is specifically used to address the issue of cold-start for new users by providing primary recommendations.Additionally, we have used two collaborative filtering approaches.The SVD-based enhances the recommendation list for the new user and tackles the sparsity problem.Simultaneously, enhanced matrix factorization with deep neural network (DNN) outperforms traditional matrix factorization in terms of recommendation diversity and accuracy.Our system improves the accuracy and effectively responds to user needs.Our approach early findings show promising results.It scores for top-10 items a total recall of (0.47), a global precision of (0.20), and an accuracy of (0.87).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".