A session generalization technique for improved web usage mining
Bibliographic record
Abstract
Generalization of web sessions is an effective approach used to overcome two major challenges in web usage mining, namely quality and scalability. Given a concept hierarchy, such as a website, generalization replaces actual page-clicks with their general concepts, i.e., nodes at higher levels. Presently known methods do this by choosing a level in the hierarchy, below which all the nodes are generalized to nodes at this level. The problem with this is that significant items may be coalesced, and insignificant ones may be retained. We present a usage driven generalization algorithm, which coalesces less significant pages into more general ones, independent of their level in the hierarchy. Based on actual usage set of sessions, item significance is estimated approximately but fast, using a small stratified sample of the large dataset. While providing scalability, the proposed generalization technique results in improved efficiency and quality of the discovered usage model, demonstrated through numerous experiments in our work.
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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 | 0.000 | 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 | 0.000 | 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 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".