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Open AccessArticle

Performance of Two Approaches of Embedded Recommender Systems

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Department of Tecnología de Computadores y Comunicaciones, Escuela Polítécnica, Universidad de Extremadura, 10003 Cáceres, Spain
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Department of Sistemas Informáticos, ETSI Sistemas Informáticos, Universidad Politécnica de Madrid, 28031 Madrid, Spain
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Author to whom correspondence should be addressed.
Electronics 2020, 9(4), 546; https://doi.org/10.3390/electronics9040546
Received: 22 February 2020 / Revised: 14 March 2020 / Accepted: 21 March 2020 / Published: 25 March 2020
(This article belongs to the Special Issue Recent Advances in Embedded Computing, Intelligence and Applications)
Nowadays, highly portable and low-energy computing environments require programming applications able to satisfy computing time and energy constraints. Furthermore, collaborative filtering based recommender systems are intelligent systems that use large databases and perform extensive matrix arithmetic calculations. In this research, we present an optimized algorithm and a parallel hardware implementation as good approach for running embedded collaborative filtering applications. To this end, we have considered high-level synthesis programming for reconfigurable hardware technology. The design was tested under environments where usual parameters and real-world datasets were applied, and compared to usual microprocessors running similar implementations. The performance results obtained by the different implementations were analyzed in computing time and energy consumption terms. The main conclusion is that the optimized algorithm is competitive in embedded applications when considering large datasets and parallel implementations based on reconfigurable hardware. View Full-Text
Keywords: embedded systems; collaborative filtering; recommender systems; parallelism; reconfigurable hardware; high-level synthesis embedded systems; collaborative filtering; recommender systems; parallelism; reconfigurable hardware; high-level synthesis
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MDPI and ACS Style

Pajuelo-Holguera, F.; Gómez-Pulido, J.A.; Ortega, F. Performance of Two Approaches of Embedded Recommender Systems. Electronics 2020, 9, 546.

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