Bitwise parallel association rule mining for web page recommendation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
For many real-life web applications, web surfers would like to get recommendation on which collections of web pages that would be interested to them or that they should follow. In order to discover this information and make recommendation, data mining---and specially, association rule mining or web mining---is in demand. Since its introduction, association rule mining has drawn attention of many researchers. Consequently, many association rule mining algorithms have been proposed for finding interesting relationships---in the form of association rules---among frequently occurring patterns. These algorithms include level-wise Apriori-based algorithms, tree-based algorithms, hyperlinked array structure based algorithms, and vertical mining algorithms. While these algorithms are popular, they suffer from some drawbacks. Moreover, as we are living in the era of big data, high volumes of a wide variety of valuable data of different veracity collected at a high velocity post another challenges to data science and big data analytics. To deal with these big data while avoiding the drawbacks of existing algorithms, we present a bitwise parallel association rule mining system for web mining and recommendation in this paper. Evaluation results show the effectiveness and practicality of our parallel algorithm---which discovers popular pages on the web, which in turn gives the web surfers recommendation of web pages that might be interested to them---in real-life web applications.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| 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 it