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相关概念视频

Self-Discrepancy Theory02:45

Self-Discrepancy Theory

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One influential perspective on what motivates people's behavior is detailed in Tory Higgin's self-discrepancy theory (Higgins, 1987). He proposed that people hold disagreeing internal representations of themselves that lead to different emotional states.  
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In general, a schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
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According to Charles Cooley, we base our image on what we think other people see (Cooley 1902). We imagine how we must appear to others, then react to this speculation. We don certain clothes, prepare our hair in a particular manner, wear makeup, use cologne, and the like—all with the notion that our presentation of ourselves is going to affect how others perceive us. We expect a certain reaction, and, if lucky, we get the one we desire and feel good about it. But more than that, Cooley...
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We have discussed why we form relationships, what attracts us to others, and different types of love. But what determines whether we are satisfied with and stay in a relationship? One theory that provides an explanation is social exchange theory. According to social exchange theory, we act as naïve economists in keeping a tally of the ratio of costs and benefits of forming and maintaining a relationship with others (Rusbult & Van Lange, 2003).
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基于社会学理论的多主题自我监督推.

Qin Zhao, Peihan Wu, Gang Liu

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    这项研究引入了一种新的社会推方法 (SMSR),该方法使用用户态度和动态主题路由来提高准确性. 它有效地解决了社交网络中的数据稀疏性和噪音问题,以提供更好的建议.

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    科学领域:

    • 计算机科学 计算机科学
    • 社交网络分析 社交网络分析
    • 人工智能的人工智能

    背景情况:

    • 社会关系为推系统提供了有价值的数据.
    • 现有的方法遭受数据稀疏和噪声,并忽视复杂的用户交互.
    • 需要先进的方法来利用社交网络的复杂性来进行增强的建议.

    研究的目的:

    • 引入一种基于社会学理论的多主题自我监督推方法 (SMSR).
    • 解决当前社会推技术的局限性,包括稀疏性,噪音和被忽视的交互细节.
    • 通过整合用户态度和动态主题建模来提高推准确性.

    主要方法:

    • 开发了基于社会理论的多主题自我监督推 (SMSR) 方法.
    • 将用户态度集成到社会关系建设中,并使用动态路由来识别主题.
    • 利用图案来揭示更高层次的用户关系,并将光图卷积网络与平衡理论结合起来.
    • 从异质图分析和社会理论中设计了四个自我监督的信号.

    主要成果:

    • SMSR有效地减轻了社会噪音,并提高了推准确度.
    • 该方法成功地识别和分类主题,揭示复杂的用户关系.
    • 在对公共数据集的广泛实验中,在最先进的方法上表现出优越的性能.

    结论:

    • 在社会推系统中,SMSR提供了显著的进步.
    • 社会学理论和自我监督学习的整合增强了社会图形信息的利用.
    • 提出的方法为克服社会推方面的挑战提供了强有力的解决方案.