AI Driven Trolley System with SMS Alerts for Seamless Customer Interaction
Bibliographic record
Abstract
Providing outstanding customer experiences is crucial in today's cutthroat service sectors. Like a ‘digital shopping trolley’ feature on a retail app or website, a ‘trolley system’ in an SMS message usually refers to a notification sent to a customer regarding their shopping trolley, most likely detailing the items currently in their trolley, their total price or a reminder to finish their purchase if they left the store without checking out. Currently, a mobile phone is seen as a tool that allows users to be contacted at any time and from any location via SMS, phone calls, or messaging apps like WhatsApp, Messenger, and others. Sometimes, this accessibility can annoy people, particularly when they receive inappropriate and unnecessary messages at the incorrect moment (e.g., meeting, lecture, workshop, etc.). Numerous firms and mobile providers send us useless SMS bundles promoting various bargains almost every day. It diverts us when we are occupied with crucial work. These advertising messages might occasionally cause us to overlook crucial messages. A prediction model for categorising SMS alerts according to user preferences is presented in this work. The BRDT methods yield the highest accuracy (91%), according to a comparison of various machine learning algorithms.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".