机器学习启发的相似度衡量从专利数据预测并购
Giambattista Albora1, Matteo Straccamore1,2, Andrea Zaccaria1,3
1Centro Ricerche Enrico Fermi, Piazza del Viminale, Rome, Italy.
PloS one
|February 6, 2026
概括
我们开发了MASS算法,用专利数据预测并购交易. 这种可解释的模型在预测专利活跃公司的并购活动时优于复杂的算法.
科学领域:
- 计算社会科学 计算社会科学
- 业务分析 业务分析
- 知识产权管理知识产权管理
背景情况:
- 由于复杂的人类因素,并购 (M&A) 预测具有挑战性.
- 现有的方法难以自动识别最佳合作伙伴或预测交易可能性.
- 存在对可解释和有效的并购预测工具的需求.
研究的目的:
- 建议和评估MASS算法用于预测并购交易.
- 适应基于专利的公司相似度指标,用于并购预测.
- 将MASS与图形卷积网络 (GCN) 方法进行比较.
主要方法:
- 开发了MASS算法,一种简化,可解释的基于树的机器学习模型.
- 利用专利数据来衡量公司的相似性.
- 将算法应用于Zephyr和Crunchbase数据集进行验证.
主要成果:
- 与GCN模型相比,MASS算法在预测并购交易方面表现优越.
- 在GCN模型中,对于具有分离专利活动的公司来说,效果更高.
- 马斯提供了一个可解释和可解释的方法来预测并购.
结论:
- 马斯提供了一个简单而强大的工具,用于模拟和预测专利活跃公司之间的并购交易.
- 该算法为战略决策中的经理和从业人员提供了宝贵的见解.
- 这项工作强调了基于专利的相似性对并购分析的有用性.
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