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CoHet4Rec: 一个关于协作异质信息网络的建议
Yao Chen1, Yuling Chen1, Zhi Ouyang1
1State Key Laboratory of Public Big Data and College of Computer Science and Technology, Guizhou University, Guiyang, China.
PloS one
|April 9, 2025
概括
本研究介绍了CoHet4Rec,这是一种新的推模型,通过使用图形神经网络 (GNN) 和协作异质信息网络 (CHIN) 来提高准确性. 它有效地解决了推系统中的冷启动问题.
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:
- 推系统 (RS) 传统上使用用户对象交互来预测偏好.
- 图形神经网络 (GNN) 通过嵌入图形数据来改进RS,但与冷启动问题作斗争.
- 社会推利用用户连接,但现有模型需要更丰富的关系探索.
研究的目的:
- 提出 CoHet4Rec,一个新的推模型.
- 解决现有推系统的局限性,包括数据稀疏性和冷启动问题.
- 通过在社交网络之外结合多样化的合作关系来提高推准确性.
主要方法:
- 开发了使用GNN的推模型CoHet4Rec.
- 构建了一个协作异质信息网络 (CHIN),具有潜在的协作异质关系因子.
- 采用因子化表示来捕捉各种用户-项目连接,并纳入外部知识.
主要成果:
- 与15种最先进的推技术 (SOTA) 相比,CoHet4Rec表现优越.
- 在关键指标方面取得了显著改善:HR@5高达31.88%,NDCG@5.39高达38.39%.
- 通过丰富的网络信息,有效地缓解了数据稀疏性和冷启动问题.
结论:
- CoHet4Rec为增强推系统提供了一个强大的解决方案.
- 该模型的灵活性允许整合各种知识来源.
- 这种方法通过捕捉复杂的用户-项目关系,显著提高了推质量.
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