Optimizing Business Sales and Improving User Experience by using Intelligent User Interface
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
This research explores the impact on the user experience when the users, that is, the people in business, are exposed to an improved version of an intelligent user interface of the review management software. Machine learning algorithms, such as Lexiconbased sentimental analysis and NRC Emotion recognition, are employed to assist the proposed review management software, Review Dock. To provide additional assistance, a Content-based Recommendation system is integrated. More than 17,000 Amazon reviews are used to generate the results. To improve the satisfaction level of the already created prototype, three iterations of usability testing were conducted on nine participants. The findings show that by following the Web Content Accessibility Guidelines (WCAG) standards, an average satisfaction score of 2.49 out of 5 on the first iteration is significantly improved to 4.9 on the last iteration. Furthermore, the polarity categorization is similar across most evaluations, which are accomplished on previously unseen data sets. However, the results also reveal that the designs will only perform well for a small-medium industry. This research attempts to fill the limitations in the literature with respect to user experience. Regardless of the tools offered, the issue for businesses in utilizing an available solution that diminishes the engaging experience remains unchanged. As a result, a new solution should solve the limits, which will directly affect the company's sales. The research question states what steps the review management software may take to reduce the overly convoluted user interface? Therefore, proposing a solution called Review Dock will provide a plethora of responses and entirely focus on customer happiness by providing a comprehensive overview of how to enhance a product's sales.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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