Exploring GTRS Based Recommender Systems with Users of Different Rating Patterns
Bibliographic record
Abstract
Recommender systems predict a new user’s opinion on a collection of items by analyzing preference information of similar users. The Pawlak rough set (PRS) model is one of the effective tools to make personalized recommendations. The game-theoretic rough set (GTRS) model improves the quality of PRS based recommendations by determining a pair of thresholds that could achieve a tradeoff between two prominent recommendation evaluation metrics, accuracy and coverage. It should be noted that the performance of a recommendation algorithm may be affected by the rating patterns of the users in the considered dataset. The aim of this research is to evaluate how the performance of the PRS based and the GTRS based recommendations vary on user groups with different rating patterns. We conducted comparative experiments on five different data samples. The experimental results suggest that compared to the PRS model, the GTRS model could not only obtain an improvement in coverage level, but also achieve an equal accuracy level on each of the considered data samples. In particular, it achieved a bigger advantage over the PRS model on user groups that make a smaller number of rating records. This performance difference indicates that compared to the PRS model, the GTRS model is a better solution to make high quality personalized recommendations on small-scale datasets with fewer rating records stored in the database.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".