Una comprensiva revisión de los métodos de recomendación basados en técnicas probabilísticas

Priscila Valdiviezo-Díaz, Antonio Hernando

Resumen


Esta investigación tiene como objetivo utilizar un método de recomendación hibrido basado en técnicas probabilísticas y de modelado de tópicos que brinde al usuario recomendaciones más ajustadas frente a los modelos de recomendación tradicionales. Este artículo presenta una revisión comprensiva de los métodos de recomendación para sistemas basado en contenido y filtrado colaborativo. Entre los métodos analizados están las Matrices de Factorización Probabilística y el método de Asignación Latente de Dirichlet. La revisión de la literatura entorno a estos modelos se centra en la identificación de problemas y cuestiones abiertas que pueden ser abarcadas para futuras investigaciones. Se analiza el funcionamiento de algunos modelos de recomendación que integran técnicas de factores latentes y de modelado de tópicos, que serán de base para comparar los resultados obtenidos con el modelo híbrido.


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