Folksonomy-boosted social media search and ranking
Bibliographic record
Abstract
With the rapid proliferation of social media services, users on the social Web are overwhelmed by the huge amount of social media available. In this paper, we look into the potential of social tagging in social media services to help users in retrieving social media. By leveraging social tagging, we propose a new personalized search method to enhance not only retrieval accuracy but also retrieval coverage. Our approach first determines the similarities between resources and between tags. Thereafter, we build two models: a user-tag relation model that reflects how a certain user has assigned tags similar to a given tag and a tag-item relation model that captures how a certain tag has been tagged to resources similar to a given resource. We then seamlessly map the tags on the items depending on a particular user's query in order to find the most attractive media content relevant to the user needs. The experimental evaluations have shown the proposed method achieves better search results than state-of-the art algorithms in terms of accuracy and coverage.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".