Users’ recognition in web using web mining techniques
Bibliographic record
Abstract
The rapid growth of the web and the lack of structure or an integrated schema create various issues to access the information for users.All users' access on web information are saved in the related server log files.The circumstance of using these files is implemented as a resource for finding some patterns of user's behavior.Web mining is a subset of data mining and it means the mining of the related data from WWW, which is categorized into three parts including web content mining, web structure mining and web usage mining, based on the part of data, which is mined.It seems necessary to have a technique, which is capable of learning the users' interests and based on the interests, which could filter the unrelated interests automatically or it could offer the related information to the user in reasonable amount of time.The web usage mining makes a profile from users to recognize them and it has direct relationship to web personalizing.The primary objective of personalizing systems is to prepare the thing, which is required by users, without asking them explicitly.In the other way, formal models prepare the possibility of system's behavior modeling.The Petri and queue nets as some samples of these models can analyze the user's behavior in web.The primary objective of this paper is to present a colored Petri net to model the user's interactions for offering a list of pages recommendation to them in web.Estimating the user's behavior is implemented in some cases like offering the proper pages to continue the browse in web, ecommerce and targeted advertising.The preliminary results indicate that the proposed method is able to improve the accuracy criterion 8.3% rather static method.
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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 | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".