A Simulation-based Approach to Evaluating the Effectiveness of Navigation Compression Models.
Bibliographic record
Abstract
Within the growing literature on web mining, there is a relatively coherent thread of ideas focused on improvements to web navigation. In this paper we focus on the idea of web usage mining, and present a general framework for deploying the mining results and evaluating the performance improvement. The generalized objects created by the application of learning methods are called Navigation Compression Models (NCMs), and we show a method for creating them and using them to make dynamic recommendations. Of note is the observation that no application of any learning method to web data makes sense without first formulating a goal framework against which that method can be evaluated. This simple idea is typically the missing ingredient of many WWW mining techniques. In this paper we present a simulation-based approach to evaluating the effectiveness of Navigation Compression Models non-intrusively by measuring the potential navigation improvement. We evaluate the improvement of user navigation using a quantitative measure called navigation improvement (NI), which indicates whether we are actually “improving ” the user’s navigation by reducing the number of hyperlinks traversed to find “relevant ” pages.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".