A Group Recommender System for Article Recommendation Using Matrix Factorization
Bibliographic record
Abstract
These days, the Internet contains an overwhelming amount of data for users looking for specific information. This is why we use recommender systems to deal with information overload problems. Among several issues, this research focuses primarily on recommending articles to users who belong to a group. The groups are pre-defined based on employees’ work roles and can be further divided into sub-groups. We use different group recommendation and sub-grouping techniques to decide which one gives optimal results. Three recommendation techniques have been applied to suggest articles to the groups, namely: Before Factorization, After Factorization, and Weighted Before Factorization. In the experiment, Weighted Before Factorization achieves the best results on our dataset, collected from a company’s internal content management system. We have also proposed an enhancement to the above group recommendation models using clustering methods to create further subgroups. Compared with the results on the original pre-defined groups, k-means sub-grouping improves the F1@5, F1@10, F1@15 by 35.75%, 19.52% and 1.54% respectively.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".