Understanding Web usage for dynamic Web-site adaptation: a case study
Bibliographic record
Abstract
Every day, new information, products and services are being offered by providers on the World Wide Web. At the same time, the number of consumers and the diversity of their interests is increasing. As a result, providers are seeking ways to infer customers' interests and to adapt their Web sites to make the content of interest more easily accessible. Pattern mining is a promising approach in support of this goal. Assuming that past navigation behavior is an indicator of users' interests, then records of this behavior, kept in the form of Web-server logs, can be mined to infer what users are interested in. On that basis, recommendations can be dynamically generated, to help new Web-site visitors find information of interest faster. In this paper, we discuss our experience with pattern mining for dynamic Web-site adaptation. Our particular approach is tailored to "focused" Web sites that offer information on a well-defined subject, such as, for example, the Web site of an undergraduate course. Visitors of such focused sites exhibit similar types of navigation behavior, corresponding to the services offered by the Web site; therefore, page recommendation based on usage-pattern mining can be quite effective.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".