What is it about?
In this paper we compare several machine learning algorithms for the task of next shopping basket recommendations to users. The Recommender System that obtained the best results is currently being used on a website that allows users to create cross-stores shopping lists and figure out the shortest and cheapest (if using a car) path to fulfill the list.
Photo by Viki Mohamad on Unsplash
Why is it important?
Next basket recommendation is a critical task in market basket data analysis. It is particularly important in grocery shopping, where grocery lists are an essential part of shopping habits of many customers. In this work, we first present a new grocery Recommender System available on the MyGroceryTour platform. The main advantage of the presented Recommender System is that our intelligent recommendation is personalized, since a separate traditional machine learning or deep learning model is built for each customer considered. Such a personalized approach allows us to outperform the prediction results provided by general state-of-the-art deep learning models.
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This page is a summary of: Intelligent personalized shopping recommendation using clustering and supervised machine learning algorithms, PLoS ONE, December 2022, PLOS,
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