国内外学术场景下个性化文本检索研究述评

张洁, 朱亮, 寇远涛

doi:10.19788/j.issn.2096-6369.230403

农业大数据学报 >

2023 , Vol. 5 >Issue 4: 24 - 36

DOI: https://doi.org/10.19788/j.issn.2096-6369.230403

研究论文

国内外学术场景下个性化文本检索研究述评

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1.中国农业科学院农业信息研究所，北京 100081
2.国家新闻出版署农业融合出版知识挖掘与知识服务重点实验室，北京 100081

张洁，E-mail:zhangjie07@caas.cn。

收稿日期: 2023-10-18

录用日期: 2023-12-08

网络出版日期: 2024-01-05

基金资助

国家社会科学基金项目(20BTQ014);中国农业科学院农业信息研究所创新工程(CAAS-ASTIP-2023-AII);国家科技文献信息中心专项(2023XM42-03)

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Research Review on Personalized Text Retrieval in the Academic Scene

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1. Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
2. Key Laboratory of Knowledge Mining and Knowledge Services in Agricultural Converging Publishing, National Press and Publication Administration, Beijing 100081, China

Received date: 2023-10-18

Accepted date: 2023-12-08

Online published: 2024-01-05

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摘要

总结国内外个性化学术文本检索的研究现状，为后续个性化学术检索的研究提供思路借鉴和前景展望。搜集国内外相关文献共计154篇，采用文献分析法归纳出个性化学术文本检索研究框架，并对核心研究与辅助研究点进行详细论述。国内外个性化学术文本检索相关研究已逐渐系统化，从理论研究走向理论与实践研究并举，目前存在低负担高隐私的交互方式尚未实现，面向认知要素的深层次个性化检索尚未实现及适用情境识别的前置研究缺失等研究问题。积极拥抱大模型等新技术赋予的能力，走向认知化、嵌入情境式及实时交互式是个性化学术文本检索的未来发展方向。

关键词： 个性化检索; 学术检索; 文本检索; 个性化文本检索

本文引用格式

张洁, 朱亮, 寇远涛 . 国内外学术场景下个性化文本检索研究述评[J]. 农业大数据学报, 2023 , 5(4) : 24 -36 . DOI: 10.19788/j.issn.2096-6369.230403

Abstract

This paper summarizes the research status of personalized academic text retrieval and provides reference and prospect for the follow-up research. We retrieved a total of 154 literature after screening and adding, used literature analysis method to summarize the research framework of personalized academic text retrieval, and discussed the core research and auxiliary research points in detail. Research on personalized academic text retrieval has been gradually systematic, moving from theoretical research to both theoretical and practical research. At present, there are some research problems, such as the low burden and high privacy interaction mode has not been realized, the deep personalized retrieval oriented to cognitive elements has not been realized, and the pre-research on appropriate context recognition is missing. The future development direction of personalized academic text retrieval is to actively embrace the ability endowed by new technologies such as large language model, and move towards cognitive-oriented, context-embedded and supporting real-time interaction.

Key words： personalized search; academic search; text search; personalized text retrieval

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