Using Clustering and Machine Learning Methods to Provide Intelligent Grocery Shopping Recommendations
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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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".