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A Comprehensive Context-Aware Recommender System Framework

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Computer Science and Engineering—Theory and Applications

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 143))

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Abstract

Context-Aware Recommender System research has realized that effective recommendations go beyond recommendation accuracy, thus research has paid more attention to human and context factors, as an opportunity to increase user satisfaction. Despite the strong tie between recommendation algorithms and the human and context data that feed them, both elements have been treated as separated research problems. This document introduces MoRe, a comprehensive software framework to build context-aware recommender systems. MoRe provides developers a set of state of the art recommendation algorithms for contextual and traditional recommendations covering the main recommendation techniques existing in the literature. MoRe also provides developers a generic data model structure that supports an extensive range of human, context and items factors that is designed and implemented following the object-oriented paradigm. MoRe saves developers the tasks of implementing recommendation algorithms, and creating a structure to support the information the system will require, proving concrete functionality, and at the same time is generic enough to allow developers adapt its features to fit specific project needs.

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Acknowledgements

This research is supported by the Maestría y Doctorado en Ciencias e Ingeniería (MYDCI) program offered by Facultad de Ciencias Químicas e Ingeniería attached to Universidad Autónoma de Baja California and for the Consejo Nacional de Ciencia y Tecnología (CONACYT) CVU 341714.

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Authors and Affiliations

  1. Universidad Autónoma de Baja California, Tijuana, Baja California, Mexico

    Sergio Inzunza & Reyes Juárez-Ramírez

Authors
  1. Sergio Inzunza
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  2. Reyes Juárez-Ramírez
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Corresponding author

Correspondence to Sergio Inzunza .

Editor information

Editors and Affiliations

  1. Calzada Universidad, Tijuana, Baja California, Mexico

    Mauricio A. Sanchez

  2. Calzada Universidad, Tijuana, Baja California, Mexico

    Leocundo Aguilar

  3. Calzada Universidad, Tijuana, Baja California, Mexico

    Manuel Castañón-Puga

  4. Calzada Universidad, Tijuana, Baja California, Mexico

    Antonio Rodríguez-Díaz

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Inzunza, S., Juárez-Ramírez, R. (2018). A Comprehensive Context-Aware Recommender System Framework. In: Sanchez, M., Aguilar, L., Castañón-Puga, M., Rodríguez-Díaz, A. (eds) Computer Science and Engineering—Theory and Applications. Studies in Systems, Decision and Control, vol 143. Springer, Cham. https://doi.org/10.1007/978-3-319-74060-7_1

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  • DOI: https://doi.org/10.1007/978-3-319-74060-7_1

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  • Print ISBN: 978-3-319-74059-1

  • Online ISBN: 978-3-319-74060-7

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