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Improving Performance of Recommendation System Architecture

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Intelligent Data Engineering and Automated Learning – IDEAL 2020 (IDEAL 2020)


The exponential appearance of online stores has implied higher market competitiveness and, consequently, companies need to adopt certain strategies to obtain greater prominence and gain clientele. This paper explores an architectural approach to incorporate a recommendation system in online stores, in order to offer a solution to achieve those goals. Developing the recommendation system infrastructure with NodeJS, based on a REST API, and according to microservices architecture concepts, has proven to be very efficient when it comes to managing great volumes of requests and data, and be capable to serve multiple tenants within a short response time. Clustering techniques were also implemented to increase the system’s performance and capability of handling requests.

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This work has been supported by FCT – Fundaçño para a Ciência e Tecnologia within the RD Units Project Scope: UIDB/00319/2020.

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Correspondence to José Machado .

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Cunha, G., Peixoto, H., Machado, J. (2020). Improving Performance of Recommendation System Architecture. In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2020. IDEAL 2020. Lecture Notes in Computer Science(), vol 12490. Springer, Cham.

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62364-7

  • Online ISBN: 978-3-030-62365-4

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