JSW 2016 Vol.11(9): 912-923 ISSN: 1796-217X
doi: 10.17706/jsw.11.9.912-923
doi: 10.17706/jsw.11.9.912-923
Modular Architecture for Recommender Systems Applied in a Brazilian e-Commerce
Allan Vidotti Prando1*, Solange Nice Alves de Souza2
1Instituto de Pesquisas Tecnológicas do Estado de SP, Cidade Universitária, Butantã, São Paulo, Brasil.
2Escola Politécnica da Universidade de São Paulo, Cidade Universitária, Butantã, São Paulo, Brasil
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. 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.
Index Terms—Big data, data mining, machine learning, recommender systems, software architecture.
2Escola Politécnica da Universidade de São Paulo, Cidade Universitária, Butantã, São Paulo, Brasil
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. 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.
Index Terms—Big data, data mining, machine learning, recommender systems, software architecture.
Cite: Allan Vidotti Prando, Solange Nice Alves de Souza, "Modular Architecture for Recommender Systems Applied in a Brazilian e-Commerce," Journal of Software vol. 11, no. 9, pp. 912-923, 2016.
General Information
ISSN: 1796-217X (Online)
Frequency: Quarterly
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, CNKI, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsweditorialoffice@gmail.com
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