Complementary Product Recommendation using Siamese Neural Network

Online catalogs on e-commerce websites are sometimes too overwhelming where customers have a choice of as much variety and richness to find what they need in one place. In e-commerce websites, recommendation systems are crucial since they enhance the user experience by assisting visitors in finding what they want by recommending products. These suggestions can be based on user traits, demographics, past purchases, or search history. In this paper, we focus on identifying a complementary relationship between products, we have made a content-based recommendation system for discovering complementary products using Siamese Neural Networks (SNN). Algorithms like this have a lot of potential to increase the average purchase amount on an e-commerce website by recommending comparable products. After implementing the network we propose an extension of the network of the SNN approach to handling more products and will improve the time for recommending products by the KNN algorithm.