用多层网络模型对电影相似性进行图形距离测量的比较
Majda Lafhel1, Hocine Cherifi2, Benjamin Renoust3
1FLSH, LRIT, FS, Mohammed V University in Rabat, Rabat 10090, Morocco.
Entropy (Basel, Switzerland)
|February 23, 2024
概括
本研究介绍了一种多层网络模型,用于使用角色,关键字和位置数据分析电影故事. 它揭示了图形距离测量如何识别类型相似性和分类电影,增强推系统.
科学领域:
- 图形理论是指图形的理论.
- 网络分析 网络分析
- 计算语言学计算语言学
背景情况:
- 图形距离测量是比较图形结构的关键.
- 电影网络分析传统上使用单层模型.
- 需要一个全面的多层网络模型来实现更丰富的故事表示.
研究的目的:
- 为电影故事开发一个多层网络模型.
- 使用层对层距离测量量来量化电影故事的相似性.
- 探索基于类型的电影分析和分类的图形距离尺度.
主要方法:
- 从电影剧本 (角色,关键字,位置) 构建多层网络.
- 应用五个图形距离尺度:网络肖像分歧,NetLSD,NetMF,拉普拉斯光谱和D尺度.
- 分析不同类型 (科幻,恐怖,浪漫,喜剧) 内的电影相似之处.
主要成果:
- 不同的图形距离测量有效地识别了同一个类型内的电影之间的相似性.
- 这些方法在基于类型的电影分类方面表现出不同程度的成功.
- 一种方法的有效性取决于它能够捕捉网络固有的结构的能力.
结论:
- 多层网络模型为电影故事分析提供了更全面的方法.
- 图形距离测量是理解基于类型的电影相似性的宝贵工具.
- 拟议的方法可以集成到电影推系统中,以提高性能.
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