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Explorar y adaptar el aumento del tiempo de prueba para la recomendación secuencial.

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    Este resumen es generado por máquina.

    El aumento del tiempo de prueba (TTA, por sus siglas en inglés) mejora la recomendación secuencial (SR, por sus siglas en inglés) sin necesidad de reentrenamiento. Los nuevos métodos, TNoise y TMask, mejoran la diversidad de datos y el rendimiento en secuencias cortas, ofreciendo una solución eficiente para la escasez de datos.

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    Área de la Ciencia:

    • La inteligencia artificial es inteligencia artificial.
    • Aprendizaje automático Aprendizaje automático.
    • Los sistemas de recomendación son sistemas de recomendación.

    Sus antecedentes:

    • El aumento de datos es crucial para abordar la escasez de datos en la recomendación secuencial (SR).
    • Los métodos existentes para aumentar el tiempo de entrenamiento requieren un costoso reentrenamiento o modificaciones arquitectónicas para modelos bien entrenados.
    • El aumento del tiempo de prueba (TTA) ofrece una alternativa prometedora al aumentar los datos durante la inferencia, evitando la sobrecarga de entrenamiento.

    Objetivo del estudio:

    • Explorar la efectividad de la ampliación del tiempo de prueba (TTA) para la recomendación secuencial (SR).
    • Identificar las limitaciones de los operadores de aumento existentes para TTA y proponer nuevas soluciones.
    • Desarrollar un método TTA eficiente y generalizable que mejore el rendimiento de SR sin un extenso reentrenamiento.

    Principales métodos:

    • Se evaluaron experimentalmente los operadores de aumento existentes (sustituto, máscara) para TTA en SR.
    • Se introdujo TNoise (inyección uniforme de ruido) y TMask (manipulación de tokens de máscara) para abordar las limitaciones de los operadores existentes.
    • Estrategias implementadas para mejorar la diversidad de datos (muestreo de proporción uniforme) y la adaptación de la longitud de la secuencia (suavizado / alargamiento para secuencias cortas, umbral para secuencias largas).

    Principales resultados:

    • Los operadores de sustitutos y máscaras mostraron potencial para TTA, manteniendo patrones secuenciales con las perturbaciones apropiadas.
    • TNoise y TMask demostraron efectividad, eficiencia y generalización en varios escenarios de SR.
    • Los métodos propuestos mejoraron la diversidad de datos y mitigaron la degradación del rendimiento tanto en secuencias cortas como largas.

    Conclusiones:

    • El aumento del tiempo de prueba (TTA) es un enfoque viable y eficiente para mejorar los modelos de recomendación secuencial.
    • Los métodos TNoise y TMask propuestos, junto con las estrategias de adaptación de la longitud de la secuencia, mejoran significativamente el rendimiento de SR.
    • Este trabajo proporciona una solución práctica para aprovechar el aumento de datos en SR sin la necesidad de reentrenamiento del modelo.