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相关概念视频

Hindsight Biases01:12

Hindsight Biases

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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

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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

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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

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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.
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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
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相关实验视频

Updated: Sep 10, 2025

Measuring Delay Discounting in Humans Using an Adjusting Amount Task
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在推系统中学习仪器变量表示

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
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PubMed
概括
此摘要是机器生成的。

这项研究引入了一种基于因果关系的新算法 (DIVRS),用于对抗推系统的偏见. 通过学习仪器变量表示,提高准确性和多样性,DIVRS有效地消除了建议.

关键词:
混偏见仪表变量潜在的混因素推系统

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科学领域:

  • 人工智能
  • 机器学习
  • 数据科学

背景情况:

  • 推系统面临着数据偏差的挑战,特别是受欢迎程度偏差和隐藏的混因素,导致不准确和不多样化的建议.
  • 现有的解散技术往往无法解决隐藏的混因素或需要预定义的仪器变量 (IV).

研究的目的:

  • 提出一种新的基于因果关系的推算法,即DIVRS,该算法直接从用户与项目的交互数据中学习仪器变量表示.
  • 在推系统中使用的图形卷积网络 (GCN) 中解决偏差放大问题.

主要方法:

  • 在推系统 (DIVRS) 中开发数据驱动的IV表示学习,以将用户行为分解为因果关系和混关系.
  • 引入了正交促进调整 (OPR) 和DIVRS特定的GCN变体 (DIVRS-GCN) 以减轻偏差放大.

主要成果:

  • DIVRS和DIVRS-GCN有效地减轻了推系统中的混偏差.
  • 这两种算法在Douban-Movie和Movielens-10M数据集上表现出优异的性能,提高了Recall@20的高达10.98%.

结论:

  • 拟议的DIVRS和DIVRS-GCN方法为推系统提供了强大而有效的解决方案.
  • 这些方法提高了推的准确性,多样性和平衡性,克服了现有的基于IV的系统的局限性.