A New Similarity Measure to Understand Visitor Behavior in a Web Site
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
The behavior of visitors browsing in a web site offers a lot of information about their requirements and the way they use the respective site. Analyzing such behavior can provide the necessary information in order to improve the web site’s structure. The literature contains already several suggestions on how to characterize web site usage and to identify the respective visitor requirements based on clustering of visitor sessions. Here we propose to combine visitor behavior with the content of the respective web pages and the similarity between different page sequences in order to define a similarity measure between different visits. This similarity serves as input for clustering of visitor sessions. The application of our approach to a bank’s web site and its visitor sessions shows its potential for internet-based businesses. key words: web mining, browsing behavior, similarity measure, clustering.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 it