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Artículos de investigación

Recomendación de locus para técnicas de factorización matricialprobabilístico

DOI: https://doi.org/10.16925/2357-6014.2021.01.10
Vol. 17 Núm. 1 (2021)
Publicado: 2021-01-11
Rachna Behl
Indu Kashyap
Introducción: El presente artículo es el resultado de la investigación “Recomendación de locus utilizando
técnicas de factorización de matrices probabilísticas” llevada a cabo en el Instituto Internacional de
Investigación y Estudios Manav Rachna, India, en el año 2019-20.
 
Metodología: La factorización matricial es una técnica colaborativa basada en modelos para recomendar
nuevos elementos a los usuarios.
 
Resultados: Los resultados experimentales en dos LBSN del mundo real mostraron que PFM supera
consistentemente a PMF. Esto se debe a que la técnica se basa en la distribución gamma para modelar la
matriz de usuario y artículo. El uso de la distribución gamma es razonable para las frecuencias de registro que son todas positivas en conjuntos de datos reales. Sin embargo, PMF se basa en una distribución gaussiana que también puede permitir valores de frecuencia negativos.
 
Conclusión: El motivo del trabajo es identificar la mejor técnica para recomendar ubicaciones con la mayor
precisión y permitir a los usuarios elegir entre una gran cantidad de ubicaciones disponibles; la mejor e
interesante ubicación según el perfil de la persona.
 
Originalidad: se ha realizado un análisis riguroso de las técnicas de factorización de matrices probabilísticas
en LBSN populares y se ha identificado la mejor técnica para la recomendación de ubicación comparando la
precisión, a saber, RMSE, Precision @ N, Recall @ N, F1 @ N de diferentes modelos.
 
Limitaciones: la información contextual del usuario, como las preferencias demográficas, sociales y geográficas, no se ha tenido en cuenta al evaluar la eficacia de las técnicas de factorización matricial probabilística para las recomendaciones de puntos de interés.
Palabras clave: Array, Array, Array, Array, Array
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[1]
R. Behl y I. Kashyap, «Recomendación de locus para técnicas de factorización matricialprobabilístico», ing. Solidar, vol. 17, n.º 1, pp. 1–25, ene. 2021, doi: 10.16925/2357-6014.2021.01.10.

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