Implementing End-User Privacy through Human Computer Interaction for Improving Quality of Personalized Web
Bibliographic record
Abstract
Users are exposed to an overwhelming amount of information in several application domains. There is a remarkable increase in the usage of Internet and general technology improvements. From this, it follows that there is a demand and need to create personalized web systems. Thus, personalized web systems are a good method of handling the flood of information and information overload, by helping people to surf the net and look for what they need. Unfortunately, the privacy issues that end-users face have not been taken seriously by some of these personalized web systems, as some of them apply it partially or fail to address the privacy issues in personalization. This affects the quality of the existing personalized web. The paper aims to investigate and explain the reasons behind the end-user’s fears in giving out his/her personal information on personalized websites. Then, based on the results modifying approach of personalized web through the use of Human Computer Interaction models to enhance the quality of the current personalized web. By addressing the main privacy issues in human computer interaction that helps the end-user trust the personalization web and website with his/her personal information and improve the quality of current personalized web. A survey study with 134 Internet users was conducted. The findings show that personalization plays different roles of attracting users in personalized websites. Internet users want useful personalized services, but at the same time, they are concerned with the manner in which firms use their data for personalization. If the involvement of Internet users with their current site is high, then personalized services are not attractive enough to motivate them to give their personal information.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".