Machine Learning Based Hybrid Association Rules for Web Usage Mining
Bibliographic record
Abstract
Online content may contain text or visual data, web mining can be used to analyze the different factors related to the online users and web contents i.e. frequency of web page usage, online user's behavior, URL visit frequency, least/most frequently content retrieval. All these are common factors for different domains i.e. education, e-commerce, marketing etc. As per the user's interest, it is necessary to recommend the URLs w.r.t. browsing history. The scalability of internet content makes it difficult to examine user behavior patterns. In case of educational domain, it is necessary to track the navigational patterns and web usage of the users, in order to provide the automated URL recommendations as per the scope of the website. In this paper, a hybrid method is presented to generate the URL usage predictions using association rules-based Markov chain and its performance is analyzed using existing approaches. Analysis shows that it outperforms in terms of accuracy, precision, recall and Fl-score value as compared to existing Markov chain method.
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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 | 0.003 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".