Bibliographic record
Abstract
Social annotation is an intuitive, on-line, collaborative process through which each element of a collection of resources (e.g., URLs, pictures, videos, etc.) is associated with a group of descriptive keywords, widely known as tags. Each such group is a concise and accurate summary of the relevant resource's content and is obtained via aggregating the opinion of individual users, as expressed in the form of short tag sequences. The availability of this information gives rise to a new searching paradigm where resources are retrieved and ranked based on the similarity of a keyword query to their accompanying tags. In this paper, we present a principled and efficient search and resource ranking methodology that utilizes exclusively the user-assigned tag sequences. Ranking is based on solid probabilistic foundations and our growing understanding of the dynamics and structure of the social annotation process, which we capture by employing powerful interpolated n -gram models on the tag sequences. The efficiency and applicability of the proposed solution to large data sets is guaranteed through the introduction of a novel and highly scalable constrained optimization framework, employed both for training and incrementally maintaining the n -gram models. We experimentally validate the efficiency and effectiveness of our solutions compared to other applicable approaches. Our evaluation is based on a large crawl of del.icio.us, numbering hundreds of thousands of users and millions of resources, thus demonstrating the applicability of our solutions to real-life, large scale systems. In particular, we demonstrate that the use of interpolated n -grams for modeling tag sequences results in superior ranking effectiveness, while the proposed optimization framework is superior in terms of performance both for obtaining ranking parameters and incrementally maintaining them.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".