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In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the...
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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Updated: Jun 11, 2025

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互动-知识语义对齐用于推的建议.

Zhen-Yu He1, Jia-Qi Lin2, Chang-Dong Wang1

  • 1School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China.

Neural networks : the official journal of the International Neural Network Society
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概括
此摘要是机器生成的。

这项研究引入了一种新的推方法 (IKSAR),该方法对用户交互和知识图之间的语义差异进行了调整. 这种方法通过考虑用户偏好来改善项目表示,优于现有的方法.

关键词:
图表神经网络的神经网络知识图表知识图表建议 建议 是一个建议.语义对齐 有意义对齐

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 数据科学数据科学数据科学

背景情况:

  • 推者系统面临着数据稀疏性的挑战.
  • 知识图表通过项目信息增强了推者.
  • 现有的方法忽略了语义差异和用户偏好.

研究的目的:

  • 为了解决知识图增强推系统的局限性.
  • 提出一种新的推方法,称为互动知识语义对准推 (IKSAR).
  • 通过减少语义差距和纳入用户偏好来改善项目表示.

主要方法:

  • 引入了一个语义对齐模块来弥合交互和知识图.
  • 在项目表示过程中集成的用户表示融合.
  • 开发了互动-知识语义对准推 (IKSAR) 方法.

主要成果:

  • 语义对齐模块减少了交互和知识图之间的差异.
  • 用户表示集成增强了融合项目的表示.
  • 在实验中,IKSAR表现出比现有方法更优异的性能.

结论:

  • IKSAR有效地克服了当前知识图增强推的局限性.
  • 该方法显示了推质量的显著改善.
  • IKSAR为未来的推者系统研究提供了一个有希望的方向.