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Más allá del sesgo de una sola perspectiva: la fusión de preferencias personalizadas y comunes para el aprendizaje
JiaXin Wu1, Guangxiong Chen1, Chenglong Pang2
1Department of Data Science and Business Intelligence, School of Management, Guangdong University of Technology, Guangzhou, China.
Resumen
Este estudio presenta P&CGCN, un nuevo sistema de recomendación que fusiona de manera única las preferencias personalizadas y comunes de los usuarios. Mejora la precisión al equilibrar de manera adaptativa estas preferencias, superando a los modelos existentes.
Área de la Ciencia:
- Inteligencia artificial
- Ciencias de la computación
- Ciencia de los datos
Sus antecedentes:
- Los sistemas de recomendación basados en la Red Convolucional de Gráficos (GCN, por sus siglas en inglés) a menudo muestran sesgo hacia elementos populares, descuidando las preferencias personalizadas del usuario.
- Los métodos existentes que suprimen la información de artículos populares pueden pasar por alto valiosas señales de preferencia común, lo que lleva a otra forma de sesgo.
Objetivo del estudio:
- Proponer P&CGCN, un marco unificado que fusione de manera colaborativa las preferencias personalizadas y comunes de los usuarios en los sistemas de recomendación.
- Abordar las limitaciones de los modelos GCN existentes evitando sesgos de una sola perspectiva.
Principales métodos:
- Desarrolló un marco unificado con agregación intra-capa y combinación inter-capa.
- Se introdujo el grado de P&C para la cuantificación adaptativa de las preferencias personales y comunes dentro de las capas.
- Profundidad de P&C diseñada para priorizar las señales de preferencia personalizadas y profundas a través de capas.
Principales resultados:
- P&CGCN demuestra un rendimiento y una eficiencia superiores a los métodos existentes en cuatro conjuntos de datos del mundo real.
- Logró aproximadamente un 20% de mejora de rendimiento sobre LightGCN en conjuntos de datos escasos y a gran escala.
- Mostró al menos una aceleración de dos veces en la eficiencia del entrenamiento.
Conclusiones:
- P&CGCN equilibra efectivamente las preferencias personalizadas y comunes, lo que lleva a recomendaciones más precisas y eficientes.
- Los mecanismos propuestos de grado y profundidad de P&C son clave para mitigar el sesgo en los recomendadores basados en GCN.
- Este enfoque ofrece un avance significativo para los sistemas de recomendación, particularmente en el manejo de la escasez de datos.
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