Hybrid approach for predicting the behavior of web users
Bibliographic record
Abstract
Hybrid approach for predicting the behavior of Web users in this paper, we propose the design and implementation of a hybrid system by combining several data mining techniques to capture user's Web browsing behavior. User navigation sessions that represent the interaction with a given Website are used to construct hypertext probability grammar (HPG). The production with high probability in HPG represents the most favorable user browsing trail. The HPG results will be further used to construct N/spl times/M matrix, and a clustering algorithm are applied to extract clusters of behaviors. N-gram model is used based on the assumption that Website visitors have limited memory of what they visited before, and the choice of the next page to visit does not depend on all pages visited previously; but only the N -1 page. N-gram will not generate strong x where |x| <N -1. The history depth is essentially an application of the N-gram mode therefore; we use these two terms interchangeably. Our experiments show the user a visual representation of site visitors' browsing behaviors. The reported results demonstrate the applicability and effectiveness of the proposed approach.
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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 | 0.001 | 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".