LINK RECOMMENDER: Collaborative-Filtering for Recommending URLs to Twitter Users
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
Twitter, the popular micro-blogging service, has gained a rapid growth in recent years. Newest information is accessible in this social web service through a large volume of real-time tweets. Tweets are short and they are more informative when they are coupled with URLs. Due to tweet overload in Twitter, we believe that an accurate URL recommender system is a beneficial tool for information seekers. In this paper, we focus on a neighborhood-based recommender system that recommends URLs to Twitter users. We consider one of the major elements of tweets, hashtags , as topic representatives of URLs in our approach. We propose methods for incorporating hashtags in measuring the relevancy of URLs. Our experiments show that our neighborhood-based recommender system outperforms the matrix factorization- based system significantly. We also show that the accuracy of URL recommendation in Twitter is time-dependent. A higher recommendation accuracy is obtained when more recent data is provided for recommendation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| 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