Construction of a Personalized Recommendation Service Model for Online Learning Resources
Bibliographic record
Abstract
In the digital age, the role of personalized learning resource recommendation system in improving learning experience and educational effect cannot be ignored. Accordingly, this article proposes a personalized recommendation service model for online learning resources to improve the accuracy, efficiency and user attention of the recommendation system. Starting with the data collection and processing of user behavior and the metadata analysis of learning resources, a recommendation algorithm based on collaborative filtering method is designed, and the content recommendation technology is applied to solve the cold start problem. This network architecture adopts micro-service architecture, which ensures the scalability and high concurrent processing ability of the system. The maximum recommendation accuracy of the system reaches 98.3%, the recall rate reaches 99.3%, the maximum response time is 895 milliseconds, and the user satisfaction reaches 8 to 9.9. This article also discusses the current challenges, such as the privacy protection of users, the transparency of recommendation and the real-time performance of the system, and puts forward relevant potential solutions, such as data encryption, enhancing the interpretability of the model and updating the recommendation model in real time. In future work, it can study how to apply deep learning to personal recommendation with higher accuracy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".