Multidimensional Factor and Cluster Analysis Versus Embedding-Based Learning for Personalized Supermarket Offer Recommendations

Abstract

Multidimensional factor and cluster analysis and embedding-based machine learning were evaluated toward a knowledge-based recommendation system for supermarket e-marketing. The goal was to produce personalized notifications on special offers, optimized per individual customer’s predicted response. To this purpose, we firstly applied Multiple Correspondence Analysis and Hierarchical Clustering to extract insights on the ordering behaviors and to identify customer classes associated with predictable preference patterns. Secondly, a neural network model based on embeddings was developed to predict the customers’ ordering actions on a personalized level at large scale. Application of the factor and cluster analysis on the Instacart dataset resulted in the identification of typical and niche patterns with prediction value. The neural network model was successfully trained to predict with satisfactory accuracy individual customers’ future orders, to be used as a basis for composing personalized recommendations.