Optimal Diversity of Recommendation List for Recommender Systems based on the Users' Desire Diversity
Bibliographic record
Abstract
Nowadays, recommender systems suggest lists of items to users considering not only accuracy but also diversity and novelty. However, suggesting the most diverse list of items to all users is not always acceptable, since different users prefer and/or tolerate different degree of diversity. Hence suggesting a personalized list with a diversity degree considering each user preference would improve the efficiency of recommender systems. The main contribution and novelty of this study is to tune the diversity degree of the recommendation list based on the users' variety-seeking feature, which ultimately leads to users' satisfaction. The proposed approach considers the similarity of users' desire diversity as a new parameter in addition to the usual similarity of users in the state-of-the-art collaborative filtering algorithm. Experimental results show that the proposed approach improves the personal diversity criterion comparing to the closest method in the literature, without decreasing accuracy.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".