Development of a Computational Recommender Algorithm for Digital Resources for Education Using Case-Based Reasoning and Collaborative Filtering

  • Guadalupe Gutiérrez
  • Lourdes Margain
  • Alberto Ochoa
  • Jesús Rojas
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 151)

Abstract

This paper describes the development proposal of a Computational Recommender Algorithm (ReCom), to support user to find the correct Digital Resource of Education (DRE), in this case the learning objects (LO) that meet the needs and study preferences of the user. The search is performed on a database that contains a collection of metadata of learning objects of different topics related to Software Architect. The algorithm ReCom proposed uses the technique Colla borative Filtering (CF) and artificial intelligence technique known as Case-Based Reasoning (CBR) using for it the framework jCOLIBRI2. The preliminary test plan is presented to evaluate the effectiveness of the recommendations for the user, considering the user profile and the value of variables of influence. It also presents the proposal of a mathematical equation to measure the degree of satisfaction of user recommendations.

Keywords

Recommender System Learning Object User Profile Collaborative Filter Artificial Intelligence Technique 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Guadalupe Gutiérrez
    • 1
  • Lourdes Margain
    • 1
  • Alberto Ochoa
    • 2
  • Jesús Rojas
    • 3
  1. 1.Universidad Politécnica de AguascalientesAguascalientesMéxico
  2. 2.Universidad Autónoma de Ciudad JuárezCiudad JuárezMéxico
  3. 3.Cloud Technologies ConsultingMexico CityMéxico

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