Create a Profile for User Using Web Usage Mining
Bibliographic record
Abstract
In this paper we try to classify navigation patterns of web users automatically; therefore, a new method is presented in order to classify the user’s navigation patterns and predict the user’s future requirements. This method is based on the mining of web server logs. Thus, in order to build user’s profile, a new method is introduced that, by registering user’s setting and similarity measure of active user to neighboring users, constructs indices implicitly and brings them up to date based on created changes. In effect, we improve the performance of recommender engine by using navigation patterns of user and clustering similar users. Furthermore, we test the precision for different inputs in the model simulated based on the neural network and we determine that if, in registering user’s profile, in addition to behavior history, current session is focused on as well, recommender engine will offer better results. This method that is based on user’s navigation patterns is capable of offering the results from recommender engines based on user’s requirement and interest. Advantage is evaluated based on two responsibilities: classification and prediction. The system has reached classification precision close to and prediction precision of about .This method can help web personalization and website better organization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".