Similarity and evaluation metrics for collaborative based recommender systems

Authors

  • Gustavo Mendoza Olguín Facultad de Ciencias de la Computación, Benemérita Universidad Autónoma de Puebla
  • Yadira Laureano De Jesús Facultad de Ciencias de la Computación, Benemérita Universidad Autónoma de Puebla
  • María Concepción Pérez de Celis Herrero Facultad de Ciencias de la Computación, Benemérita Universidad Autónoma de Puebla

DOI:

https://doi.org/10.36825/RITI.07.14.019

Keywords:

Recommender Systems, Collaborative Filtering, Similarity Metrics

Abstract

Recommender Systems are smart systems that bring to the users a set of personalized suggestions from a specific type of items(objects). In order to do this, several techniques are used to collect the user’s characteristics
for, using data processing, to find a subset of items that could be relevant to him. The improvement of the
recommendation’s accuracy is crucial because offering relevant content (based on needs or likes) to the visitors of web sites, mainly commercial ones, is trending. This article shows a comparative analysis among different similarity and evaluation metrics proposed for collaborative-filtering based recommender systems; doing tests on commonly used datasets to determine its efficiency on production time.

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Published

2019-11-14

How to Cite

Mendoza Olguín, G., Laureano De Jesús, Y., & Pérez de Celis Herrero, M. C. (2019). Similarity and evaluation metrics for collaborative based recommender systems. Revista De Investigación En Tecnologías De La Información, 7(14), 224–240. https://doi.org/10.36825/RITI.07.14.019