GKB: A Predictive Analytics Framework to Generate Online Product Recommendations

Recommender Systems are essential to many of the largest internet companies' core products. Online users today expect sites that offer a large assortment of products to also serve recommendations. These recommendations are based on various pieces of data including user ratings of products and product features. In this paper, we explore the use case of applying foundational recommender system techniques and algorithms to provide book recommendations. First, we introduce what recommender systems are and the different types. Then, we will describe the Greenquist-Kilitcioglu-Bari (GKB) framework, an end to end process of building out a fully functional and live recommendation system that can be hosted on the internet, which has an RMSE of 0.842. Steps of the process that will be highlighted are data collection and preprocessing, model selection and evaluation, combining different models to create a hybrid model, and hosting the models on a live website that can serve recommendations in real time to many users. We also use the trained model to serve recommendations to a new user that was not part of the training process. This approach creates promises beyond book recommendations and can be applied to marketing, finance, politics, e-commerce, and any data matching applications.