An intelligent shopping list based on the application of partitioning and machine learning algorithms

Abstract

A grocery list is an integral part of the shopping experience of many consumers. Several mobile retail studies of grocery apps indicate that potential customers place the highest priority on features that help them to create and manage personalized shopping lists. First, we propose a new machine learning model written in Python 3 that predicts which grocery products the consumer will buy again or will try to buy for the first time, and in which store(s) the purchase will be made. Second, we introduce a smart shopping template to provide consumers with a personalized weekly shopping list based on their shopping history and known preferences. As the explanatory variables, we used available grocery shopping history, weekly product promotion information for a given region, as well as the product price statistics.

Intelligent personalized shopping recommendation using clustering and supervised machine learning algorithms

“…It is important to note that in terms of F-score our personalized RNN-GRU model outperformed the recent general LSTM-based model proposed by Tahiri et al [6] by 0.339 when we used the new data available on the MyGroceryTour platform. Furthermore, for the augmented data considered by Tahiri et al, our F-score result was 0.189 higher than that of Tahiri and coauthors.…”

“…Finally, Tahiri et al [6] have recently proposed to use both recurrent and feedforward neural networks that were combined to non-negative matrix factorization and gradient boosting trees in order to build intelligent grocery baskets for the users of the MyGroceryTour platform. Tahiri et al considered different features and much less real customers (compared to our study) to describe the behavior of the MyGroceryTour users.…”

“…MyGroceryTour (http://mygrocerytour.ca) is a good example of such a recommender system. MyGroceryTour is a Canadian shopping database and website that allows users to manage their grocery lists based on available weekly promotions in most major grocery stores located in their area [6].…”

Retail Customer and Market Proclivity Assessment using Historical data and Social Media Analytics

“…Tahiri, N. et al, 2019 also worked on a problem that is very similar to ours. Their main focus was to analyze customer behavior by predicting the purchase patterns.…”

“…Overview: Tahiri, N. et al, 2019 worked on a novel machine learning model compiled in python 3. Their proposed solution aimed to predict the purchase-repurchase of the products by the consumers.…”