超越准确性:基于图形神经网络的推系统中的多样性,偶然性和公平性的审查
Tomislav Duricic1,2, Dominik Kowald1,2, Emanuel Lacic3
1Institute of Interactive Systems and Data Science, Graz University of Technology, Graz, Austria.
Frontiers in big data
|January 4, 2024
概括
本综述探讨了图形神经网络 (GNN) 推系统,超越准确性,增强多样性,偶然性和公平性,以提高用户参与度. 它在个性化建议中检查了GNN的挑战和未来方向.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 推系统对于在线平台至关重要,图形神经网络 (GNN) 在准确性方面表现出色.
- 传统的评估侧重于准确性,忽视了其他重要方面,如多样性,偶然性和公平性.
研究的目的:
- 审查基于GNN的推系统,而不仅仅是以准确性为中心的评估.
- 探索促进多样性,偶然性和公平性的方法.
- 确定实际挑战和未来的研究方向.
主要方法:
- 对基于GNN的推系统的最新文献的审查.
- 分析模型开发阶段:数据预处理,图形构造,嵌入,传播,融合,评分和培训.
- 讨论在平衡准确度与超准确度指标方面的实际挑战.
主要成果:
- 最近的进展解决了准确性-多样性权衡问题,并增强了偶然性和公平性.
- 不同的GNN模型开发阶段提供了整合这些超准确度维度的机会.
- 实际实施面临的挑战是同时优化准确性,多样性,偶然性和公平性.
结论:
- 未来的研究应该专注于开发强大的GNN推系统,考虑多个性能维度.
- 为了提高用户满意度和参与度,需要一种超越单维准确性的整体方法.
- 本综述提供了对基于GNN的推系统中多方面的问题的全面了解.
关键词:
超越了精确度的准确性.多样性的多样性多样性的多样性公平的公平的公平.图形神经网络的神经网络新鲜感 新奇性 新奇性推者系统是推者系统.这是一种serendipity.调查调查调查调查调查调查调查调查更多相关视频
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