Clickstream Prediction Using Sequential Stream Mining Techniques with Markov Chains
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
As one of data mining tasks, sequential pattern mining provides valuable information about frequent patterns of users over time. For instance, frequent sequential patterns can be applicable to analyze user clickstreams for determination of web navigation patterns, genome sequences, and customer purchasing patterns. In many real-life situations, data to be mined are continuously changing. Moreover, these data are streaming at a high velocity, which leads to impracticality of storing all these data in memory. Hence, to handle these situations, we propose three stream mining algorithms to first find frequent sequential patterns. The algorithms then form statistical models, which are stored as Markov chains or transition matrices capturing frequent sequential patterns mined so far, to predict future user clickstream (e.g., the web page the user will visit next). Experimental results show the efficiency and prediction accuracy of our proposed Markov chain-based sequential stream mining algorithms in clickstream prediction.
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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 | 0.000 | 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 | 0.001 |
| 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 it