通过社交网络平衡用户偏好:一种以条件为导向的社会推模型,用于减轻受欢迎偏见
Xin He1, Wenqi Fan2, Ruobing Wang1
1The Department of Computer Science and Technology, the School of Artificial Intelligence (SAI), at Jilin University, 2699 Qianjin Street, Chang Chun, 130000, Jilin, China.
概括
本研究介绍了CGSoRec,这是一种新的社会推模型,通过消除社交网络和调整用户偏好来减少人气偏见. 实验表明CGSoRec有效地减轻了对更好的建议的偏见.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 数据挖掘 数据挖掘
背景情况:
- 社会推模型利用用户连接进行个性化.
- 现有的模型受到人气偏见和冗余的社会信息的影响.
- 人气偏见导致过度推受欢迎的项目和低推的利基 (长尾) 项目.
研究的目的:
- 提出一个以条件为导向的社会推模型 (CGSoRec),以解决社会推中的受欢迎偏见.
- 拒绝社交网络并调整用户的社交偏好,以提高推准确度.
- 为了减轻社交网络固有的受欢迎偏见的放大.
主要方法:
- CGSoRec采用条件导向的社会拒绝模型 (CSD) 来过无关紧要的社会联系.
- 它根据被拒绝的社交网络计算和调整用户的社交偏好.
- 一种条件导向的扩散推模型 (CGD) 使用调整的偏好来指导基于条件的推.
主要成果:
- 在三个真实数据集上进行了全面的实验,验证了CGSoRec.的有效性.
- 提出的方法成功地减轻了社会推中的受欢迎偏见.
- 与现有的社会推技术相比,CGSoRec表现得更好.
结论:
- CGSoRec有效地减少了社会推系统中的受欢迎偏见.
- 拒绝社交网络和调整偏好权重是脱节的关键.
- 该模型为更公平,更多样化的建议提供了一个有希望的方法.
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