Agricultural product E-commerce recommendation system based on neural factorization machine in smart cities

Abstract

Recently, e-commerce becomes a new way to sell agricultural products, such as orange, apple, tea, rice. For the farmers, they can obtain more earnings by avoid intermediate links. For the customers, they have more choices and can buy what they want. The e-commerce directly links farmers and customers together and recommends personalized agricultural products to customers. In order to recommend proper agricultural products to customers, we propose an agricultural product e-commerce recommendation system in which neural factorization Machine (NFM) is used as recommendation algorithm. NFM combines factorization machine (FM) and neural network (NN) and captures high-order information between features. Therefore, the generalization of NFM is better than previous recommendation algorithms. Experimental results show that NFM can improve the recommendation accuracy of the agricultural product e-commerce system about 3%-4%.