Optimizing Business Sales and Improving User Experience by using Intelligent User Interface
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.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".