一个对社交信息敏感的模型,用于对话推系统
Abdulaziz Mohammed1, Mingwei Zhang1, Gehad Abdullah Amran2
1College of Software Engineering, Northeastern University, Shenyang, China.
PeerJ. Computer science
|September 24, 2025
概括
本研究引入了对话推系统 (CRS) 的新框架,该框架使用社会信息和对比学习 (CL) 来改进推. 通过有效地将社会背景与对话历史相结合,SISSF方法提高了个性化和相关性.
科学领域:
- 人工智能的人工智能
- 人与计算机的交互
- 信息检索 信息检索
背景情况:
- 对话推系统 (CRS) 允许自然语言交互进行项目建议.
- 整合对话历史与用户的社会信息仍然是语义融合的一个挑战.
- 现有的方法很难有效地弥合不同数据源之间的语义差距.
研究的目的:
- 在对话数据集中开发一个用于提取和利用社会信息的创新框架.
- 引入一种新的社会信息敏感语义融合 (SISSF) 方法.
- 通过纳入社会背景,增强CRS建议的个性化和相关性.
主要方法:
- 通过推断评分来提取社会信息,并构建用户-item/用户-用户交互图.
- 在SISSF方法中使用对比学习 (CL) 来弥合语义差距.
- 评估ReDial和INSPIRED数据集的框架,使用自动和人为指标.
主要成果:
- 在两种数据集上,SISSF表现出与基线模型相比的显著改进.
- 在推任务 (例如,R@1,R@50) 和对话质量 (例如,不同的指标) 中取得了卓越的表现.
- 人类评估证实流利性和信息性明显改善.
结论:
- 拟议的SISSF框架有效地将社会背景纳入CRS.
- 对比式学习是对话建议中语义融合的一个可行的方法.
- 将社会背景纳入对话系统中显著增强了个性化和相关性.
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