How News Recommendation System Effects User’s Individual and Aggregate Topic Diversity—A Study of Simulation Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper is to explore the influence of recommendation system on users’ topic diversity. We construct a simulation system that covers the news recommendation closed-loop processes of user profile generation, news generation and delivery, user selection and browsing, and user profile update. Quantitative indicators were formulated to assess users’ topic diversity, encompassing both individual and aggregate dimensions. Then simulation compared the effects on users’ topic diversity across three news delivery methods. The results show that compared with the delivery method of self-selection, filtering-based recommendation and popular recommendation significantly increase users’ individual topic diversity while having obstructive effects on users’ aggregate topic diversity. Various recommendation algorithms and ways to update user profiles have slight impacts and do not change relationships in trends. Lastly, we discussed the relevant topics in conjunction with our conclusions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it