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DRG: Un marco de gráficos relacionales duales para la recomendación de cursos

Yong Ouyang1, Zhen Ye1, Lingyu Chen1

  • 1College of Computer Science, Hubei University of Technology, Wuhan, 430068, PR China.

Neural networks : the official journal of the International Neural Network Society
|August 22, 2025
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio introduce un marco de gráfico de relación dual (DRG) para combatir la escasez de datos en los sistemas de recomendación de cursos educativos. DRG mejora la precisión mediante el modelado de relaciones duales, superando los enfoques de un solo gráfico.

Palabras clave:
Recomendación del cursoGráfico de las relaciones de cursoGráfico de relaciones dualesGrandes modelos de lenguaje

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Área de la Ciencia:

  • Tecnología educativa
  • Inteligencia artificial
  • Ciencia de los datos

Sus antecedentes:

  • Los sistemas de recomendación de cursos son cruciales para el aprendizaje personalizado y la mejora de la calidad de la enseñanza.
  • Los grandes modelos de lenguaje (LLM) son prometedores, pero luchan con la escasez de datos.
  • La escasez de datos limita la precisión de los modelos de recomendación tradicionales y basados en LLM.

Objetivo del estudio:

  • Proponer un marco de gráfico de relación dual (DRG) para abordar la escasez de datos en la recomendación del curso.
  • Modele las relaciones curso-curso y usuario-curso para mejorar la precisión de las recomendaciones.
  • Desarrollar una solución escalable y efectiva para recomendaciones de cursos personalizados en entornos educativos escasos.

Principales métodos:

  • Construir un gráfico basado en cursos utilizando el razonamiento semántico de LLM, el filtrado colaborativo, el agrupamiento y la minería de reglas de asociación.
  • Construir un gráfico basado en el usuario a través del filtrado colaborativo y la inferencia de preferencias de LLM.
  • Integración de gráficos duales a través del aprendizaje conjunto y el razonamiento colaborativo dentro de una tubería unificada.

Principales resultados:

  • El marco DRG alivió significativamente la escasez de datos, aumentando la cobertura del enlace en un 37,88% y un 12,67% en dos conjuntos de datos.
  • El DRG demostró un rendimiento superior en la clasificación de tareas en comparación con los enfoques de relación única.
  • El módulo DRG propuesto mejoró tanto los sistemas de recomendación tradicionales como los basados en el LLM.

Conclusiones:

  • El marco del gráfico de doble relación (DRG) aborda efectivamente la escasez de datos en los sistemas de recomendación educativa.
  • El modelado de relaciones duales e integración de la comprensión semántica impulsada por LLM conduce a una mayor precisión de las recomendaciones.
  • DRG es un módulo versátil, plug-and-play que mejora los modelos de recomendación existentes y ofrece una solución escalable.