Locus recommendation using probabilistic matrix factorization techniques

Rachna Behl; Indu Kashyap

doi:10.16925/2357-6014.2021.01.10

Recomendación de locus para técnicas de factorización matricialprobabilístico

Artículos de investigación

https://doi.org/10.16925/2357-6014.2021.01.10

Rachna Behl Ver Biografía

Manav Rachna International Institute of Research and Studies

Indu Kashyap Ver Biografía

Department of Computer Science and Engineering

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: filtrado colaborativo, filtrado de información, LBSN, factorización matricial, PDI

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R. Behl and I. Kashyap, “Recomendación de locus para técnicas de factorización matricialprobabilístico”, ing. Solidar, vol. 17, no. 1, pp. 1–25, Jan. 2021, doi: 10.16925/2357-6014.2021.01.10.

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Vol. 17 Núm. 1 (2021)

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2021-01-11

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