A new way of finding better neighbors in recommendation systems based on collaborative filtering

Remigio Ismael Hurtado Ortiz, Domenica Alejandra Merchan Garcia, Alejandro Sebastian Enriquez Mancheno

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución de conferenciarevisión exhaustiva

1 Cita (Scopus)

Resumen

One of the biggest problems of the internet is information overload. A way to handle this is Collaborative Filtering. However, it can present problems as they work with large rating matrices, and they are always really sparse. In this paper, we purpose a model that finds the closest neighbors efficiently incorporating dimensionality reduction, using Truncated Singular Value Decomposition which helps with sparse data and avoids noise caused by lack of ratings, then using clustering as we have a dense reduced matrix, and finally applying the correct similarity metric to improve predictions. To evaluate the prediction quality we use the mean absolute error. The experiments are executed with MovieLens 1M Open Data Set. And to explain the model we use a running example, named datatoy.

Idioma originalInglés
Título de la publicación alojada2019 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2019
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781728128986
DOI
EstadoPublicada - nov 2019
Evento2019 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2019 - Ixtapa, Guerrero, México
Duración: 13 nov 201915 nov 2019

Serie de la publicación

Nombre2019 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2019

Conferencia

Conferencia2019 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2019
País/TerritorioMéxico
CiudadIxtapa, Guerrero
Período13/11/1915/11/19

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© 2019 IEEE.

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