Product Recommendation System for Supermarket

Customers who seek the services at supermarkets are subjected to inconsistencies & ambiguities over choosing their desired products from a wide range of products with the closest quality. Meanwhile, supermarkets find it very difficult to satiate the customers' demand. Therefore, proposing a method to analyze the customers' need plays an important role in attracting new and regular customers. The purpose of this study is to formulate a product recommendation system which analyze customers' needs and thus recommend the best products. This system recommends products to the regular customers and to the new customers as well. New customers mean obviously the customers with no purchasing history at the supermarket in question. The system referred to recommends the products to the new customers using up two methods. One method recommends the most popular products while the other method solely focuses on the product description for recommendation. The system recommends the products to the regular customers using up user-based collaborative filtering, item based collaborative filtering and association rule mining. It recommends products to regular customers based on purchasing history and priority ratings given by other users who bought the products. Initially, the recommendation algorithm finds a set of customers who purchased and rated the products that overlap with the user who purchased and rated the products. The algorithm aggregates products from the customers with similar preference and eliminates the products the user has already purchased or rated. The proposed methodology improves the shopping experience of customers by recommending accurately and efficiently the products that are personalized to the need of the customers.