通过个性化基于个人资料的重新排名来提高对话文献检索质量
IEEE journal of biomedical and health informatics
|March 3, 2026
概括
我们开发了对话文学个性化重新排名 (CLPR),以改善对话中的学术文献检索. 通过根据用户的搜索历史和对话背景来个性化结果,CLPR提高了搜索准确性.
科学领域:
- 信息科学 信息科学 信息科学
- 计算机科学 计算机科学
- 生物医学信息学 生物医学信息学
背景情况:
- 学术文献检索面临着信息过载和证据短缺的挑战.
- 在多轮对话中,代查询的精细化加剧了这些检索困难.
研究的目的:
- 提出一个个性化的框架,对话文学个性化重新排名 (CLPR),用于在对话环境中有效地检索学术文献.
- 解决信息过载和证据短缺之间的紧张关系在多轮学术研究中.
主要方法:
- CLPR将密集的语义检索与个性化的用户配置文件统一起来.
- 广泛的,高回忆度的检索识别了候选文件.
- 对话历史被压缩成一个使用大型语言模型的文本配置文件,编码顺序连续性,立即关注和长期研究背景.
- 神经交叉编码器将该配置文件作为伪查询用于最终的文档排名.
主要成果:
- 在LitSearch (计算机科学) 基准测试中,CLPR表现出强大的概括性,获得0.4793.10的NDCG@10.
- 在MedCorpus基准指标上,CLPR取得了最先进的表现,P@1 = 0.9497和NDCG@10 = 0.9271.
- 废弃性研究表明,长期的背景线索是最有影响的,保持最新的配置文件比静态配置文件提高了性能.
结论:
- 在多轮对话设置中,CLPR提供准确和个性化的文献检索.
- 该框架通过克服信息检索挑战,有效地加速跨科学领域的证据综合.
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