Modular Architecture for Recommender Systems Applied in a Brazilian e-Commerce

Over the last decade, recommender systems have been widely applied by major e-commerce websites for personalized user experience. However, few efforts have been focused so far on recommender systems architecture. In addition, Big Data technologies present opportunities to create unprecedented business advantage and better service delivery at low cost. The recommender system architecture may vary according to the context in which e-commerce is inserted and with the adopted business settings. Consequently, from smaller to bigger companies, each recommendation system has his individual architecture with distinct implementations, but sharing similar issues. Therefore, providing a software architecture which can be easily understood, implemented and extended if necessary, would help any companies to build their own efficient recommender system, contributing to maintaining and expanding their business. In this case, is also important indicates what the technology is better tailored to each point of the architecture, considering that expertise might not exists. Modular and extensible recommender system architecture for e-commerce is proposed here. This architecture is prepared to handle a large volume of data, responding to user actions in real time and enabling the development and testing of new approaches and recommendation technologies. All layers and components of the proposed architecture are described, including technologies to fit in these components, considering the advantages of big data and open-source possibilities. Finally, as an example, the architecture implementation in a real case scenario is shown in a Brazilian e-commerce.