A Real-time Targeted Recommender System for Supermarkets

Abstract

Supermarket customers find it difficult to choose from a large variety of products or be informed for the latest offers that exist in a store based on the items that they need or wish to purchase. This paper presents a framework for a Recommender System deployed in a supermarket setting with the aim of suggesting real-time personalized offers to customers. As customers navigate in a store, iBeacons push personalized notifications to their smart-devices informing them about offers that are likely to be of interest. The suggested approach combines an Entropy-based algorithm, a Hard k-modes clustering and a Bayesian Inference approach to notify customers about the best offers based on their shopping preferences. The proposed methodology improves the customer's overall shopping experience by suggesting personalized items with accuracy and efficiency. Simultaneously, the properties of the underlying techniques used by the proposed framework tackle the data sparsity, the cold-start problem and other scalability issues that are often met in Recommender Systems. A preliminary setup in a local supermarket confirms the validity of the proposed methodology, in terms of accuracy, outperforming the traditional Collaborative Filtering approaches of user-based and item-based.