SMART PICK: A Smart Gadget Recommendation System
Bibliographic record
Abstract
Smart Pick is a mobile first recommendation system designed to help users efficiently find the most suitable smart-phone or laptop. With the fast growth of technology, hundreds of gadgets are available in the market, and users often struggle to choose the right one. Many people spend hours browsing e-commerce websites, reading reviews, or watching comparison videos before making a purchase. This process is time consuming and often confusing for non-technical users. The proposed system, Smart Pick, reduces this problem by providing quick recommendations based on budget (INR) and user preferences such as gaming, camera, battery life, portability, or overall performance. The app is developed using Android Studio for the frontend and MySQL as the backend database. All gadget details such as specifications, pricing, and images are stored in a MySQL database. To generate recommendations, the application uses a a Rule-Based Filtering approach paired with a Content-Based Scoring model that matches user preferences against the dataset. The system then highlights the top three options, presenting each with an image, cost, key specs, and a brief explanation. This design helps users assist decision-making and with greater clarity. Testing with a sample dataset of smartphones and laptops showed that the app delivers accurate recommendations quickly. Smart Pick demonstrates how a lightweight recommendation tool can be built using accessible platforms like Android Studio and MySQL. Future upgrades may include live price updates, an expanded dataset, favorites, and comparison features.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.027 |
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".