Comparing Tag Clouds, Term Histograms, and Term Lists for Enhancing Personalized Web Search
Bibliographic record
Abstract
Although static ranked lists remain the dominant Web search interface, they can limit the ability of Web searchers to find desired information when it is buried deep in the collection of search results. Web search visualization and Web search personalization are two active research directions that have shown promise for improving the user experience while searching the Web. In this paper, we propose three methods for visually representing information available within a personalized Web search system: tag clouds, term histograms, and term lists. These approaches not only describe the personalization model to the searcher, but also support interactive re-ranking of the search results. While preliminary evaluations did not find improvements beyond what was achieved with the personalization system, measures of subjective reactions showed an increased satisfaction in the search results. These results indicate that visual and interactive methods can be valuable for providing searchers with a sense of awareness and control during the search process.
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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 | 0.001 | 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 | 0.001 | 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 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".