Adaptive Weblog Post Filtering Based on User Browsing History
Bibliographic record
Abstract
Weblogs are one of important Web-based services that establish the foundations of the Web 2.0. These days many companies propose free Weblog hosting and population services, because they realize that Weblogs are evolving to be a more topic based systems, and so they could be good places to gain more revenue. One of effective factors for gaining more revenue is blog popularity. Weblogs have posts that arranged chronologically with most recent first. Some of Weblog has many posts that cause difficulty in finding specific posts by each viewer. On the other hand unsuitable posts order can redouble this problem. This difficulty occasions reduction of Weblog popularity. As our researches on Farsi weblogs shown many viewers close some Weblog windows before completely loaded in their own Web browsers. Based on our research an important reason is the existence of vast amount of Weblog posts. Viewer interests can be beneficial if they utilize for adaptive filtering Weblogs and showing their contents. This paper represents an approach for filtering and reordering Weblog posts based on user’s browsing history Experimental results show that our filtering approach can improve Weblog popularity and can increases Weblog viewers.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".