情报研究

科研信息用户与知识服务平台个性化信息推荐功能的交互研究

  • 张建伟 ,
  • 李月琳 ,
  • 张泰瑞
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  • 1 郑州航空工业管理学院信息管理学院 郑州 450046;
    2 南开大学商学院信息资源管理系 天津 300071;
    3 南开大学信息行为科学研究中心 天津 300071
张建伟,讲师,博士,硕士生导师;张泰瑞,博士。

收稿日期: 2023-05-12

  修回日期: 2023-07-26

  网络出版日期: 2023-11-08

基金资助

本文系国家社会科学基金重大项目“国家重大突发事件信息公开质量研究”(项目编号:20&ZD142)研究成果之一。

Research on Interaction Between Researchers and Knowledge Service Platform’s Personalized Information Recommendation

  • Zhang Jianwei ,
  • Li Yuelin ,
  • Zhang Tairui
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  • 1 School of Information Management, Zhengzhou University of Aeronautics, Zhengzhou 450046;
    2 Department of Information Resources Management, Business School, Nankai University, Tianjin 300071;
    3 Research Center for Information Behavior, Nankai University, Tianjin 300071

Received date: 2023-05-12

  Revised date: 2023-07-26

  Online published: 2023-11-08

摘要

[目的/意义] 开展科研信息用户与知识服务平台个性化信息推荐功能的交互研究,有助于提升对用户推荐交互行为的理解,改善平台的推荐交互设计,从而提升推荐功能的用户体验;也可为优化推荐算法,提升推荐内容有效性提供指导。[方法/过程] 招募36名参与者,采用实验研究方法,以CNKI和ScienceDirect为实验平台,利用Morae收集数据,开展用户研究。[结果/结论] 研究揭示用户与推荐功能的交互行为、交互路径与交互路径链。主要揭示用户与个性化信息推荐交互的4个阶段、9种交互行为特征及4种交互路径类型;用户对推荐的关注源于检索结果无法满足他们的信息需求。研究发现采纳推荐能够启发用户,进一步影响他们与平台的交互。研究也表明,当前知识服务平台的推荐质量有待提升,用户对推荐的采纳使用率较低,常用的推荐交互路径较短,趋于简单化。

本文引用格式

张建伟 , 李月琳 , 张泰瑞 . 科研信息用户与知识服务平台个性化信息推荐功能的交互研究[J]. 图书情报工作, 2023 , 67(20) : 105 -115 . DOI: 10.13266/j.issn.0252-3116.2023.20.010

Abstract

[Purpose/Significance] The research on interaction between researchers (also users in this study) and personalized information recommendation (PIR) will help IR systems developers recognize their recommendation preference, improve the platforms’ recommendation interaction design, and enhance user experience. It can also provide guidance for optimizing recommendation algorithms and improve the recommendation effectiveness. [Method/Process] An experiment was conducted on CNKI and ScienceDirect with 36 participants, and the data was collected and analyzed by Morae. [Result/Conclusion] This study reveals users interaction behaviors when using PIR, interaction process, and interaction path between users and PIR. The results indicate that there existed four stages, nine types of interaction behavior, and four types of interaction paths. Users will adopt PIR when the searching results cannot satisfy their information needs. The results also indicate that adopting PIR can inspire users and further affect their interaction with the platforms. The study also shows that the current quality of PIR provided by the platforms needs to be improved. Users’ adoption rate of PIR is low. In addition, the interaction paths are short and tend to be simplified.

参考文献

[1] LAZER D, KENNEDY R, KING G, et al. The parable of Google flu:traps in big data analysis.[J]. Science, 2014, 343(3):1203- 1205.
[2] 张建伟, 李月琳, 卢丹. 科研人员的个性化推荐需求研究[J]. 图书与情报, 2021(4):52-62. (ZHANG J W, LI Y L, LU D. On personalized recommendation needs of researchers[J]. Library and information, 2021(4):52-62.)
[3] 李月琳. 数字图书馆用户多维交互与评估[M]. 北京:国家图书馆出版社, 2019. (LI Y L. Multi-dimensional user interaction and evaluation for digital libraries[M]. Beijing:National Library of China Publishing House, 2019.)
[4] 于良芝. 图书馆情报学概论[M]. 北京:国家图书馆出版社, 2016.(YU L Z. Introduction to library and information science[M]. Beijing:National Library of China Publishing House, 2016.)
[5] YANG N, JO J, JEON M, et al. Semantic and explainable research-related recommendation system based on semisupervised methodology using BERT and LDA models[J]. Expert systems with application, 2022, 190(3):116209.
[6] GUO S, ZHANG W, ZHANG S. A PageRank-based collaborative filtering recommendation approach in digital libraries[J]. Technical gazette, 2017, 24(4):1051-1058.
[7] SHARMA S, RANA V, KUMAR V. Deep learning based semantic personalized recommendation system[J]. International journal of information management data insights, 2021, 1(2):100028.
[8] 李言, 陈远平. 科研信息门户的资源推荐技术研究[J]. 数据与计算发展前沿, 2021, 3(2):112-119. (LI Y, CHEN Y P. Research on resource recommendation technology of scientific research information portal[J]. Frontiers of data & computing, 2021, 3(2):112-119.)
[9] HUANG J, OOSTERHUIS H, RIJKE M, et al. Keeping dataset biases out of the simulation:a debiased simulator for reinforcement learning based recommender systems[C]//Proceedings of the 14th ACM conference on recommender systems. Brazil (virtual event):ACM, 2020:190-199.
[10] YANG C, PAN J, GAO X, et al. Cross-task knowledge distillation in multi-task recommendation[C]//Proceedings of the thirty-sixth AAAI conference on artificial intelligence. Palo Alto (virtual event):AAAI, 2022:4318-4326.
[11] QIN Y, WANG P, LI C. The world is binary:contrastive learning for denoising next basket recommendation[C]//Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval. New York (virtual event):ACM, 2021:859-868.
[12] ZHAO X, XIA L, ZOU L, et al. UserSim:User simulation via supervised generative adversarial network[C]//Proceedings of the web conference 2021. Ljubljana:ACM, 2021:3582-3589.
[13] ZHANG Y, FENG F, HE X, et al. Causal intervention for leveraging popularity bias in recommendation[C]//Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval. New York (virtual event):ACM, 2021:11-20.
[14] WANG P, LI L, WANG R, et al. Learning persona-driven personalized sentimental representation for review-based recommendation[J]. Expert systems with applications, 2022, 203(10):117317.
[15] ZHENG J, MA Q, GU H, et al. Multi-view denoising graph autoencoders on heterogeneous information networks for cold-start recommendation[C]//Proceedings of the 27th ACM SIGKDD conference on knowledge discovery and data mining. Singapore:ACM, 2021:2338-2348.
[16] CHOU Y, CHEN C, HUANG S. Modeling behavior sequence for personalized fund recommendation with graphical deep collaborative filtering[J]. Expert systems with application, 2022, 192(15):116311.
[17] 李贺, 侯力铁, 祝琳琳. 移动图书馆情景感知信息推荐服务用户接受行为研究[J]. 图书情报工作, 2019, 63(12):94-104. (LI H, HOU L T, ZHU L L. Research on user acceptance behavior of awareness-based information recommendation service in mobile library[J]. Library and information service, 2019, 63(12):94-104.)
[18] WAN H, YU S. A recommendation system based on an adaptive learning cognitive map model and its effects[J]. Interactive learning environments, 2020, 31(3):1821-1839.
[19] 武慧娟, 孙鸿飞. 基于认知计算与情境感知的个性化信息自适应推荐模式框架研究[J]. 情报科学, 2018, 36(5):114- 118, 143. (WU H J, SUN H F. Research on the framework of personalized information self-adaptive recommendation model based on cognitive computing and context awareness[J]. Information science, 2018, 36(5):114-118, 143.)
[20] 查先进, 张坤, 严亚兰. 数字图书馆智能信息推荐服务满意度影响机理的扎根研究[J]. 情报学报, 2022, 41(1):83- 95. (ZHA X J, ZHANG K, YAN Y L. Grounded research on the impacting mechanism of satisfaction with intelligent information recommendation services by digital libraries[J]. Journal of the China Society for Scientific and Technical Information, 2022, 41(1):83-95.)
[21] 陈煜东. 基于用户画像的商品推荐研究[D]. 南昌:东华理工大学, 2020. (CHEN Y D. Research on product recommendation based on user portrait[D]. Nanchang:East China University of Science and Technology, 2020.)
[22] 吴树芳, 吴崇崇, 朱杰. 基于兴趣转移的微博用户动态画像生成[J]. 情报科学, 2021, 39(8):103-111. (WU S F, WU C C, ZHU J. Microblog user dynamic portrait generation based on interest transfer[J]. Information science, 2021, 39(8):103-111.)
[23] 刘海鸥, 黄文娜, 谢姝琳. 面向情境化推荐服务的图书馆用 户画 像研 究[J]. 图书 馆学 研究, 2018(20):62-68. (LIU H O, HUANG W N, XIE S L. Research on the situational recommendation-oriented library user profiles[J]. Research on library science, 2018(20):62-68.)
[24] 凌坤, 姜久雷, 李盛庆. 基于改进用户画像的协同过滤推荐算法[J]. 计算机仿真, 2022, 39(12):533-541. (LING K, JIANG J L, LI S Q. Collaborative filtering recommendation algorithm based on improved user profile[J]. Computer simulation, 2022, 39(12):533-541.)
[25] 何胜, 熊太纯, 吴智勤. 基于标签计算的高校图书馆学者画像及知识推荐研究[J]. 图书馆研究, 2022, 52(4):89-97. (HE S, XIONG T C, WU Z Q. Research on scholar portrait of university libraries and knowledge recommendation based on label calculation[J]. Library research, 2022, 52(4):89-97.)
[26] 张建伟, 李月琳, 李东东. 网络学术资源平台个性化推荐服务特征研究[J]. 情报资料工作, 2021, 42(5):76-83. (ZHANG J W, LI Y L, LI D D. Research on the characteristics of personalized recommendation service on the network academic resource platform[J]. Information and documentation services, 2021, 42(5):76-83.)
[27] 李月琳, 张昕. 数字图书馆交互评估:从理论构建到工具开发[J]. 大学图书馆学报, 2018, 36(2):59-70. (LI Y L, ZHANG X. Developing a tool for evaluating interaction in digital libraries[J]. Journal of academic libraries, 2018, 36(2):59-70.)
[28] BORLUND P. Experimental components for the evaluation of interactive information retrieval systems[J]. Journal of documentation, 2000, 56(1):71-90.
[29] 全国哲学社会科学工作办公室. 2020年度国家社会科学基金重大项目招标公告[EB/OL].[2023-05-09]. http://www.nopss.gov.cn/n1/2020/0727/c219469-31799628.html.(National Office for Philosophy and Social Sciences. National social sciences foundation key projects bidding announcement 2020[EB/OL].[2023-05-09]. http://www.nopss.gov.cn/n1/2020/0727/c219469-31799628.html.)
[30] KELLY D. Methods for evaluating interactive information retrieval systems with users[M]. Hanover:Now Publishers Inc. 2009.
[31] 李醒民. 知识的三大部类:自然科学、社会科学和人文学科[J]. 学术界, 2012(8):5-33, 286. (LI X M. Three divisions of knowledge:natural science, social science and the humanities[J]. Academics, 2012(8):5-33, 286.)
[32] 张晓娟, 彭琳, 李倩. 查询推荐研究综述[J]. 情报学报, 2019, 38(4):432-446. (ZHANG X J, PENG L, LI Q. A review of query suggestion[J]. Journal of the China Society for Scientific and Technical Information, 2019, 38(4):432-446.)
[33] 石乐昊, 寇月, 申德荣, 等. 面向HIN基于多视角嵌入融合的推荐方法[J]. 软件学报, 2022, 33(10):3619-3634. (SHI L H, KOU Y, SHEN D R, et al. Recommendation method based on multi-view embedding fusion for HINs[J]. Journal of software, 2022, 33(10):3619-3634.)
[34] SAVAGE-KNEPSHIELD P, BELKIN N. Interaction in information retrieval:trends over time[J]. Journal of the American Society for Information Science & Technology, 2010, 50(8):1067-1082.
[35] ZIPF K. Human behavior and the principle of least effort[M]. Cambridge:Addison-Wesley, 1949.
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