提升知识图表与多元意识的意图推断推的建议
Shaoqing Lv1, Chichi Wang1, Ju Xiang2
1School of Communication and Information Engineering, Xi'an University of Posts and Telecommunications, Xian, China.
概括
本研究介绍了KGDII (Knowledge Graph with Diverse-Aware Intent Inference) 框架,通过产生多样化的用户意图来改进推系统. KGDII提高了推的质量和多样性,优于现有的方法.
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:
- 知识图 (KG) 通过语义结构来增强推系统.
- 像协作过这样的传统方法也有局限性.
- 图形神经网络 (GNN) 模拟KG关系,但面临信息冗余和有限多样性的挑战.
研究的目的:
- 提出一种新的框架,即以多元意识意图推断 (KGDII) 的知识图,以解决基于GNN的推系统的局限性.
- 提高建议的质量和多样性.
- 通过选择多样化的关系和利用注意力机制来改善用户意图推断.
主要方法:
- 开发了知识图与多元意识意图推断 (KGDII) 框架.
- 实施了一种机制,用于在KG中选择各种各样的关系子集,以生成用户意图.
- 利用注意力机制来优先考虑重要关系,减少冗余并改善意图表示.
主要成果:
- 在现实数据集上,KGDII在建议准确性和多样性方面表现优于最先进的方法.
- 废弃性研究证实了拟议成分的有效性.
- 案例分析强调了KGDII框架的强烈可解释性.
结论:
- 通过推断不同的用户意图,KGDII有效地提高了推质量和多样性.
- 该框架的注意力机制和多样化的关系选择有助于提高性能和减少冗余.
- 对于推系统的性能和可解释性,KGDII提出了一种有前途的方法.
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