Inge-Cuc (Jan 2017)

Implementation of the k -Neighbors Technique in a recommender algorithm for a purchasing system using NFC and Android

  • Oscar Arley Riveros,
  • Juan Guillermo Romero,
  • Jhon Francined Herrera

DOI
https://doi.org/10.17981/ingecuc.13.1.2017.01
Journal volume & issue
Vol. 13, no. 1
pp. 9 – 18

Abstract

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Introduction: This paper aims to present the design of a mobile application involving NFC technology and a collaborative recommendation algorithm under the K-neighbors technique, allowing to observe personalized suggestions for each client. Objective: Design and develop a mobile application, using NFC technologies and K-Neighbors Technique in a recommendation algorithm, for a Procurement System. Methodology: The process followed for the design and development of the application focuses on: • Review of the state of the art in mobile shopping systems. • State-of-the-art construction in the use of NFC technology and AI techniques for recommending systems focused on K-Neighbors Algorithms • Proposed system design • Parameterization and implementation of the K-Neighbors Technique and integration of NFC Technology • Proposed System Implementation and Testing. Results: Among the results obtained are detailed: • Mobile application that integrates Android, NFC Technologies and a Technique of Algorithm Recommendation • Parameterization of the K-Neighbors Technique, to be used within the recommended algorithm. • Implementation of functional requirements that allow the generation of personalized recommendations for purchase to the user, user ratings Conclusions: The k-neighbors technique in a recommendation algorithm allows the client to provide a series of recommendations with a level of security, since this algorithm performs calculations taking into account multiple parameters and contrasts the results obtained for other users, finding the articles with a Greater degree of similarity with the customer profile. This algorithm starts from a sample of similar, complementary and other unrelated products, applying its respective formulation, we obtain that the recommendation is made only with the complementary products that obtained higher qualification; Making a big difference with most recommending systems on the market, which are limited to suggest the best-selling, best qualified or in the same category.

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