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Hindsight Biases01:12

Hindsight Biases

3.9K
Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now? 
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Decision Making: P-value Method01:09

Decision Making: P-value Method

5.7K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
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Confirmation Biases01:31

Confirmation Biases

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The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
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Regression Toward the Mean01:52

Regression Toward the Mean

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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Decision Making: Traditional Method01:14

Decision Making: Traditional Method

4.1K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
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Randomized Experiments01:13

Randomized Experiments

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
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Video Experimental Relacionado

Updated: Sep 10, 2025

Measuring Delay Discounting in Humans Using an Adjusting Amount Task
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Measuring Delay Discounting in Humans Using an Adjusting Amount Task

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Aprendizaje de la representación de la variable instrumental para el desbloqueo en los sistemas de recomendación

Zhirong Huang1, Shichao Zhang1, Debo Cheng2

  • 1organization=Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, addressline=Guangxi Normal University, city=Guilin, postcode=541004, state=Guangxi, country=China; organization=Guangxi Key Lab of Multi-Source Information Mining and Security, addressline=Guangxi Normal University, city=Guilin, postcode=541004, state=Guangxi, country=China.

Neural networks : the official journal of the International Neural Network Society
|August 26, 2025
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio introduce un nuevo algoritmo basado en la causalidad (DIVRS) para combatir el sesgo en los sistemas de recomendación. DIVRS elimina efectivamente las recomendaciones mediante el aprendizaje de representaciones de variables instrumentales, mejorando la precisión y la diversidad.

Palabras clave:
Prejuicio de confusiónVariable instrumentalLos factores de confusión latentesSistemas de recomendación

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Área de la Ciencia:

  • Inteligencia artificial
  • Aprendizaje automático
  • Ciencia de los datos

Sus antecedentes:

  • Los sistemas de recomendación se enfrentan a desafíos con sesgos de datos, particularmente sesgos de popularidad y factores de confusión latentes, que conducen a sugerencias inexactas y menos diversas.
  • Las técnicas de desbaste existentes a menudo no abordan los factores de confusión latentes o requieren variables instrumentales predefinidas (IV).

Objetivo del estudio:

  • Proponer un nuevo algoritmo de recomendación basado en la causalidad, DIVRS, que aprenda las representaciones de variables instrumentales directamente de los datos de interacción usuario-artículo.
  • Para abordar el problema de la amplificación de sesgo en las redes de convoluciones de gráficos (GCN) utilizadas en los sistemas de recomendación.

Principales métodos:

  • Aprendizaje de representación IV basado en datos para el desbloqueo en el sistema de recomendación (DIVRS) para descomponer el comportamiento del usuario en relaciones causales y de confusión.
  • Se introdujo la regularización de la promoción ortogonal (OPR) y una variante de GCN específica de DIVRS (DIVRS-GCN) para mitigar la amplificación de sesgo.

Principales resultados:

  • El DIVRS y el DIVRS-GCN mitigan efectivamente el sesgo de confusión en los sistemas de recomendación.
  • Ambos algoritmos demostraron un rendimiento superior a los métodos de última generación en los conjuntos de datos de Douban-Movie y Movielens-10M, mejorando Recall@20 hasta en un 10,98%.

Conclusiones:

  • Los enfoques DIVRS y DIVRS-GCN propuestos ofrecen soluciones sólidas y eficaces para desactivar los sistemas de recomendación.
  • Estos métodos mejoran la precisión, la diversidad y el equilibrio de las recomendaciones, superando las limitaciones de los sistemas existentes basados en IV.