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.
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.
How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".