Serverless E-commerce Recommendation System

Abstract

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. With the rapid development of e-commerce, its information structure is becoming more and more complex, and the amount of information is becoming larger and larger. Users are often lost in massive commodity information, and merchants cannot establish effective customer relationships in massive user information. In order to improve the service level and market competitiveness of Internet commerce, many e-commerce websites begin to introduce cloud computing technology. According to users' purchase records and historical browsing records, they can find the goods they like and recommend them to users. In order to manage massive commodity information and user information more efficiently, this paper proposes a solution to build e-commerce recommendation system on the cloud computing platform to improve the ability of massive data mining and business intelligence analysis, and realise high-performance computing at a lower cost.