Using Clustering and Machine Learning Methods to Provide Intelligent Grocery Shopping Recommendations
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
Abstract Nowadays, grocery lists make part of shopping habits of many customers. With the popularity of e-commerce and plethora of products and promotions available on online stores, it can become increasingly difficult for customers to identify. products that both satisfy their needs and represent the best deals overall. In this paper, we present a grocery recommender system based on the use of traditional machine learning methods aiming at assisting customers with creation of their grocery lists on the MyGroceryTour platform which displays weekly grocery deals in Canada. Our recommender system relies on the individual user purchase histories, as well as the available products’ and stores’ features, to constitute intelligent weekly grocery lists. The use of clustering prior to supervised machine learning methods allowed us to identify customers profiles and reduce the choice of potential products of interest for each customer, thus improving the prediction results. The highest average F-score of 0.499 for the considered dataset of 826 Canadian customers was obtained using the Random Forest prediction model which was compared to the Decision Tree, Gradient Boosting Tree, XGBoost, Logistic Regression, Catboost, Support Vector Machine and Naive Bayes models in our study.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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