Analysis of Web‐usage behavior for focused Web sites: a case study
Bibliographic record
Abstract
Abstract The number of Web users and the diversity of their interests increase continuously; Web‐content providers seek to infer these interests and to adapt their Web sites to improve accessibility of the offered content. Usage‐pattern mining is a promising approach in support of this goal. Assuming that past navigation behavior is an indicator of the users' interests, then, Web‐server logs can be mined to infer what the users are interested in. On that basis, the Web site may be reorganized to make the interesting content more easily accessible or recommendations can be dynamically generated to help new visitors find information of interest faster. In this paper, we discuss a case study examining the effectiveness of sequential‐pattern mining for understanding the users' navigation behavior in focused Web sites. This study examines the Web site of an undergraduate course, as an example of a focused Web site that offers information intrinsically related to a process and closely reflects the workflow of this underlying process. We found that in such focused sites, indeed, visitor behavior reflects the process supported by the Web site and that sequential‐pattern mining can effectively predict Web‐usage behavior in these sites. Copyright © 2004 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".